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: "cycle consistent deep generative hashing for cross modal retrieval"
  Abstract: In this paper, we propose a novel deep generative approach to cross-modal retrieval to learn hash functions in the absence of paired training samples through the cycle consistency loss. Our proposed approach employs adversarial training scheme to learn a couple of hash functions enabling translation between modalities while assuming the underlying semantic relationship. To induce the hash codes with semantics to the input-output pair, cycle consistency loss is further delved into the adversarial training to strengthen the correlation between the inputs and corresponding outputs. Our approach is generative to learn hash functions, such that the learned hash codes can maximally correlate each input–output correspondence and also regenerate the inputs so as to minimize the information loss. The learning to hash embedding is thus performed to jointly optimize the parameters of the hash functions across modalities as well as the associated generative models. Extensive experiments on a variety of large-scale cross-modal data sets demonstrate that our proposed method outperforms the state of the arts.

Connected through hyperedge HE-1 (induced by source paper "few shot deep adversarial learning for video based person re identification"):
  with members:
    - "3 d personvlad learning deep global representations for video based person reidentification"
    - "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"
    - "crossing generative adversarial networks for cross view person re identification"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "person re identification via recurrent feature aggregation"
    - "learning deep neural networks for vehicle re id with visual spatio temporal path proposals"
    - "unsupervised person re identification clustering and fine tuning"

Connected through hyperedge HE-2 (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"
    - "weakly supervised complementary parts models for fine grained image classification from the bottom up"
    - "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"
    - "what and where to match deep spatially multiplicative integration networks for person re identification"

Connected through hyperedge HE-3 (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"
    - "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"
    - "effective multi query expansions collaborative deep networks for robust landmark retrieval"

Connected through hyperedge HE-4 (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"
    - "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"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "ensemble emotion recognizing with multiple modal physiological signals"):
  with members:
    - "cross entropy adversarial view adaptation for person re identification"
    - "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-6 (induced by source paper "multi modal deep analysis for multimedia"):
  with members:
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "better and faster knowledge transfer from multiple self supervised learning tasks via graph distillation for video classification"
    - "visual question reasoning on general dependency tree"
    - "learning conditioned graph structures for interpretable visual question answering"
    - "sch gan semi supervised cross modal hashing by generative adversarial network"
    - "self supervised adversarial hashing networks for cross modal retrieval"
    - "inferring and executing programs for visual reasoning"
    - "mutan multimodal tucker fusion for visual question answering"

Connected through hyperedge HE-7 (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"
    - "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"
    - "crossing generative adversarial networks for cross view person re identification"

Connected through hyperedge HE-8 (induced by source paper "3 d personvlad learning deep global representations for video based person reidentification"):
  with members:
    - "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"
    - "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)