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: "adversarial metric attack for person re identification"
  Abstract: Person re-identification (re-ID) has attracted much attention recently due to its great importance in video surveillance. In general, distance metrics used to identify two person images are expected to be robust under various appearance changes. However, our work observes the extreme vulnerability of existing distance metrics to adversarial examples, generated by simply adding human-imperceptible perturbations to person images. Hence, the security danger is dramatically increased when deploying commercial re-ID systems in video surveillance, especially considering the highly strict requirement of public safety. #R##N#Although adversarial examples have been extensively applied for classification analysis, it is rarely studied in metric analysis like person re-identification. The most likely reason is the natural gap between the training and testing of re-ID networks, that is, the predictions of a re-ID network cannot be directly used during testing without an effective metric. In this work, we bridge the gap by proposing Adversarial Metric Attack, a parallel methodology to adversarial classification attacks, which can effectively generate adversarial examples for re-ID. Comprehensive experiments clearly reveal the adversarial effects in re-ID systems. Moreover, by benchmarking various adversarial settings, we expect that our work can facilitate the development of robust feature learning with the experimental conclusions we have drawn.

Connected through hyperedge HE-1 (induced by source paper "universal adversarial perturbations against person re identification"):
  with members:
    - "unsupervised adversarial attacks on deep feature based retrieval with gan"
    - "joint discriminative and generative learning for person re identification"
    - "learning with average precision training image retrieval with a listwise loss"
    - "metric attack and defense for person re identification"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "re identification with consistent attentive siamese networks"
    - "parameter free spatial attention network for person re identification"

Connected through hyperedge HE-2 (induced by source paper "learning transferable adversarial examples via ghost networks"):
  with members:
    - "node injection attacks on graphs via reinforcement learning"
    - "adversarial attacks and defences competition"
    - "generating adversarial examples with adversarial networks"
    - "improving transferability of adversarial examples with input diversity"
    - "low frequency adversarial perturbation"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "snapshot ensembles train 1 get m for free"
    - "convolutional networks with adaptive inference graphs"

Connected through hyperedge HE-3 (induced by source paper "adversarial binary coding for efficient person re identification"):
  with members:
    - "deep semantic hashing with generative adversarial networks"
    - "features for multi target multi camera tracking and re identification"
    - "hard aware point to set deep metric for person re identification"
    - "binary generative adversarial networks for image retrieval"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "hashnet deep learning to hash by continuation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"

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)