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: "cross entropy adversarial view adaptation for person re identification"
  Abstract: Person re-identification (re-ID) is a task of matching pedestrians under disjoint camera views. To recognise paired snapshots, it has to cope with large cross-view variations caused by the camera view shift. Supervised deep neural networks are effective in producing a set of non-linear projections that can transform cross-view images into a common feature space. However, they typically impose a symmetric architecture, yielding the network ill-conditioned on its optimisation. In this paper, we learn view-invariant subspace for person re-ID, and its corresponding similarity metric using an adversarial view adaptation approach. The main contribution is to learn coupled asymmetric mappings regarding view characteristics which are adversarially trained to address the view discrepancy by optimising the cross-entropy view confusion objective. To determine the similarity value, the network is empowered with a similarity discriminator to promote features that are highly discriminant in distinguishing positive and negative pairs. The other contribution includes an adaptive weighing on the most difficult samples to address the imbalance of within/between-identity pairs. Our approach achieves notable improved performance in comparison to state-of-the-arts on benchmark datasets.

Connected through hyperedge HE-1 (induced by source paper "attribute guided feature learning network for vehicle re identification"):
  with members:
    - "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"
    - "weakly supervised complementary parts models for fine grained image classification from the bottom up"
    - "cityflow a city scale benchmark for multi target multi camera vehicle tracking and 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"

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"
    - "pre training with whole word masking for chinese bert"
    - "roberta a robustly optimized bert pretraining approach"
    - "deep learning for sentiment analysis a survey"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "ensemble emotion recognizing with multiple modal physiological signals"):
  with members:
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "iterative views agreement an iterative low rank based structured optimization method to multi view spectral clustering"

Connected through hyperedge HE-4 (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"
    - "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"
    - "crossing generative adversarial networks for cross view person re identification"

Connected through hyperedge HE-5 (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"
    - "cityflow a city scale benchmark for multi target multi camera vehicle tracking and 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"

Connected through hyperedge HE-6 (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"
    - "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"
    - "where and when to look deep siamese attention networks for video based person re identification"

Connected through hyperedge HE-7 (induced by source paper "purifying real images with an attention guided style transfer network for gaze estimation"):
  with members:
    - "3 d personvlad learning deep global representations for video based person reidentification"
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "person re identification with metric learning using privileged information"
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "guiding intelligent surveillance system by learning by synthesis gaze estimation"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "effective multi query expansions collaborative deep networks for robust landmark retrieval"

Connected through hyperedge HE-8 (induced by source paper "a fast online cascaded regression algorithm for face alignment"):
  with members:
    - "3 d personvlad learning deep global representations for video based person reidentification"
    - "a robust approach for securing audio classification against adversarial attacks"
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "face alignment in full pose range a 3d total solution"
    - "robust tracking via weighted online extreme learning machine"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "a functional regression approach to facial landmark tracking"
    - "effective multi query expansions collaborative deep networks for robust landmark retrieval"

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)