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: "few shot deep adversarial learning for video based person re identification"
  Abstract: Video-based person re-identification (re-ID) refers to matching people across camera views from arbitrary unaligned video footages. Existing methods rely on supervision signals to optimise a projected space under which the distances between inter/intra-videos are maximised/minimised. However, this demands exhaustively labelling people across camera views, rendering them unable to be scaled in large networked cameras. Also, it is noticed that learning effective video representations with view invariance is not explicitly addressed for which features exhibit different distributions otherwise. Thus, matching videos for person re-ID demands flexible models to capture the dynamics in time-series observations and learn view-invariant representations with access to limited labeled training samples. In this paper, we propose a novel few-shot deep learning approach to video-based person re-ID, to learn comparable representations that are discriminative and view-invariant. The proposed method is developed on the variational recurrent neural networks (VRNNs) and trained adversarially to produce latent variables with temporal dependencies that are highly discriminative yet view-invariant in matching persons. Through extensive experiments conducted on three benchmark datasets, we empirically show the capability of our method in creating view-invariant temporal features and state-of-the-art performance achieved by our method.

Connected through hyperedge HE-1 (induced by source paper "discriminative feature and dictionary learning with part aware model for vehicle re identification"):
  with members:
    - "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"
    - "effective multi query expansions collaborative deep networks for robust landmark retrieval"

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:
    - "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"
    - "bert pre training of deep bidirectional transformers for language understanding"

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:
    - "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"
    - "crossing generative adversarial networks for cross view person re identification"

Connected through hyperedge HE-4 (induced by source paper "eliminating cross camera bias 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"
    - "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-5 (induced by source paper "deep visual waterline detection within inland marine environment"):
  with members:
    - "3 d personvlad learning deep global representations for video based person reidentification"
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "effective multi query expansions collaborative deep networks for robust landmark retrieval"
    - "iterative views agreement an iterative low rank based structured optimization method to multi view spectral clustering"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "co regularized multi view sparse reconstruction embedding for dimension reduction"):
  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"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "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"
    - "iterative views agreement an iterative low rank based structured optimization method to multi view spectral clustering"

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)