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 attribute image person re identification"
  Abstract: While attributes have been widely used for person re-identification (Re-ID) which aims at matching the same person images across disjoint camera views, they are used either as extra features or for performing multi-task learning to assist the image-image matching task. However, how to find a set of person images according to a given attribute description, which is very practical in many surveillance applications, remains a rarely investigated cross-modality matching problem in person Re-ID. In this work, we present this challenge and formulate this task as a joint space learning problem. By imposing an attribute-guided attention mechanism for images and a semantic consistent adversary strategy for attributes, each modality, i.e., images and attributes, successfully learns semantically correlated concepts under the guidance of the other. We conducted extensive experiments on three attribute datasets and demonstrated that the proposed joint space learning method is so far the most effective method for the attribute-image cross-modality person Re-ID problem.

Connected through hyperedge HE-1 (induced by source paper "interactive natural language based person search"):
  with members:
    - "an empirical study of person re identification with attributes"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "identity aware textual visual matching with latent co attention"
    - "co attending free form regions and detections with multi modal multiplicative feature embedding for visual question answering"
    - "person search with natural language description"
    - "mask r cnn"
    - "deep attributes driven multi camera person re identification"
    - "vqa visual question answering"

Connected through hyperedge HE-2 (induced by source paper "improving person re identification by attribute and identity learning"):
  with members:
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "attention aware compositional network for person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "deep spatial feature reconstruction for partial person re identification alignment free approach"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "person search with natural language description"
    - "looking beyond appearances synthetic training data for deep cnns in re identification"
    - "end to end comparative attention networks for person re identification"

Connected through hyperedge HE-3 (induced by source paper "beyond intra modality a survey of heterogeneous person re identification"):
  with members:
    - "illumination adaptive person re identification"
    - "multi level factorisation net for person re identification"
    - "harmonious attention network for person re identification"
    - "re identification with consistent attentive siamese networks"
    - "attention aware compositional network for person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "densely semantically aligned person re identification"
    - "deep spatial feature reconstruction for partial person re identification alignment free approach"

Connected through hyperedge HE-4 (induced by source paper "attkgcn attribute knowledge graph convolutional network for person re identification"):
  with members:
    - "learning context graph for person search"
    - "aanet attribute attention network for person re identifications"
    - "multi label image recognition with graph convolutional networks"
    - "attributes aided part detection and refinement for person re identification"
    - "pedestrian re identification based on tree branch network with local and global learning"
    - "hierarchical graph convolutional networks for semi supervised node classification"
    - "perceive where to focus learning visibility aware part level features for partial person re identification"
    - "improving person re identification by attribute and identity learning"

Connected through hyperedge HE-5 (induced by source paper "attributes guided feature learning for vehicle re identification"):
  with members:
    - "pedestrian attribute recognition a survey"
    - "improving person re identification by attribute and identity learning"
    - "person re identification by camera correlation aware feature augmentation"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "person search with natural language description"
    - "camera style adaptation for person re identification"
    - "a pose sensitive embedding for person re identification with expanded cross neighborhood re ranking"

Connected through hyperedge HE-6 (induced by source paper "one pass person re identification by sketch online discriminant analysis"):
  with members:
    - "unsupervised person re identification by deep asymmetric metric embedding"
    - "person re identification by camera correlation aware feature augmentation"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "scalable person re identification on supervised smoothed manifold"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "unsupervised adaptive re identification in open world dynamic camera networks"
    - "deep self paced learning for person re identification"
    - "svdnet for pedestrian 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)