human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "effective multi query expansions collaborative deep networks for robust landmark retrieval"
  Abstract: Given a query photo issued by a user (q-user), the landmark retrieval is to return a set of photos with their landmarks similar to those of the query, while the existing studies on the landmark retrieval focus on exploiting geometries of landmarks for similarity matches between candidate photos and a query photo. We observe that the same landmarks provided by different users over social media community may convey different geometry information depending on the viewpoints and/or angles, and may subsequently yield very different results. In fact, dealing with the landmarks with \illshapes caused by the photography of q-users is often nontrivial and has seldom been studied. In this paper we propose a novel framework, namely multi-query expansions, to retrieve semantically robust landmarks by two steps. Firstly, we identify the top-$k$ photos regarding the latent topics of a query landmark to construct multi-query set so as to remedy its possible \illshape. For this purpose, we significantly extend the techniques of Latent Dirichlet Allocation. Then, motivated by the typical \emph{collaborative filtering} methods, we propose to learn a \emph{collaborative} deep networks based semantically, nonlinear and high-level features over the latent factor for landmark photo as the training set, which is formed by matrix factorization over \emph{collaborative} user-photo matrix regarding the multi-query set. The learned deep network is further applied to generate the features for all the other photos, meanwhile resulting into a compact multi-query set within such space. Extensive experiments are conducted on real-world social media data with both landmark photos together with their user information to show the superior performance over the existing methods.

Connected through hyperedge HE-1 (induced by source paper "discriminative feature and dictionary learning with part aware model for vehicle re identification"):
  with members:
    - "few shot deep adversarial learning for video based person re identification"
    - "3 d personvlad learning deep global representations for video based person reidentification"
    - "learning hybrid representation by robust dictionary learning in factorized compressed space"
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "cityflow a city scale benchmark for multi target multi camera vehicle tracking and re identification"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "ram a region aware deep model for vehicle re identification"
    - "where and when to look deep siamese attention networks for video based person re identification"

Connected through hyperedge HE-2 (induced by source paper "a bert based sentiment analysis and key entity detection approach for online financial texts"):
  with members:
    - "few shot deep adversarial learning for video based person re identification"
    - "ernie enhanced language representation with informative entities"
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "albert a lite bert for self supervised learning of language representations"
    - "cross entropy adversarial view adaptation for person re identification"
    - "pre training with whole word masking for chinese bert"
    - "roberta a robustly optimized bert pretraining approach"
    - "deep learning for sentiment analysis a survey"

Connected through hyperedge HE-3 (induced by source paper "pac gan an effective pose augmentation scheme for unsupervised cross view person re identification"):
  with members:
    - "few shot deep adversarial learning for video based person re identification"
    - "3 d personvlad learning deep global representations for video based person reidentification"
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "cross entropy adversarial view adaptation for person re identification"
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "what and where to match deep spatially multiplicative integration networks for person re identification"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "sem gan semantically consistent image to image translation"

Connected through hyperedge HE-4 (induced by source paper "3 d personvlad learning deep global representations for video based person reidentification"):
  with members:
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "towards good practices on building effective cnn baseline model for person re identification"
    - "what and where to match deep spatially multiplicative integration networks for person re identification"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "diversity regularized spatiotemporal attention for video based person re identification"
    - "person re identification via recurrent feature aggregation"
    - "scalable person re identification on supervised smoothed manifold"

Connected through hyperedge HE-5 (induced by source paper "cycle consistent deep generative hashing for cross modal retrieval"):
  with members:
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "what and where to match deep spatially multiplicative integration networks for person re identification"
    - "deep semantic hashing with generative adversarial networks"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "zero shot sketch image hashing"
    - "cm gans cross modal generative adversarial networks for common representation learning"
    - "cycada cycle consistent adversarial domain adaptation"
    - "hashgan attention aware deep adversarial hashing for cross modal retrieval"

Connected through hyperedge HE-6 (induced by source paper "eliminating cross camera bias for vehicle re identification"):
  with members:
    - "few shot deep adversarial learning for video based person re identification"
    - "a dual path model with adaptive attention for vehicle re identification"
    - "3 d personvlad learning deep global representations for video based person reidentification"
    - "sinet a scale insensitive convolutional neural network for fast vehicle detection"
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "cross entropy adversarial view adaptation for person re identification"
    - "cityflow a city scale benchmark for multi target multi camera vehicle tracking and re identification"
    - "improving person re identification by attribute and identity learning"

Connected through hyperedge HE-7 (induced by source paper "cross domain knowledge learning with dual branch adversarial network for vehicle re identification"):
  with members:
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "cross entropy adversarial view adaptation for person re identification"
    - "improving person re identification by attribute and identity learning"
    - "what and where to match deep spatially multiplicative integration networks for person re identification"
    - "dart domain adversarial residual transfer networks for unsupervised cross domain image classification"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "unsupervised cross dataset person re identification by transfer learning of spatial temporal patterns"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"

Connected through hyperedge HE-8 (induced by source paper "a targeted acceleration and compression framework for low bit neural networks"):
  with members:
    - "3 d personvlad learning deep global representations for video based person reidentification"
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "what and where to match deep spatially multiplicative integration networks for person re identification"
    - "from hashing to cnns training binaryweight networks via hashing"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "towards accurate binary convolutional neural network"
    - "deep learning with low precision by half wave gaussian quantization"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.CV(Computer Vision and Pattern Recognition)