human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "person re identification with bias controlled adversarial training"):
  Abstract: Inspired by the effectiveness of adversarial training in the area of Generative Adversarial Networks we present a new approach for learning feature representations in person re-identification. We investigate different types of bias that typically occur in re-ID scenarios, i.e., pose, body part and camera view, and propose a general approach to address them. We introduce an adversarial strategy for controlling bias, named Bias-controlled Adversarial framework (BCA), with two complementary branches to reduce or to enhance bias-related features. The results and comparison to the state of the art on different benchmarks show that our framework is an effective strategy for person re-identification. The performance improvements are in both full and partial views of persons.

Includes members:
  Member "harmonious attention network for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond")
  Member "attention aware compositional network for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "magnifiernet towards semantic regularization and fusion for person re identification")
  Member "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on feature normalization and data augmentation")
  Member "exploring disentangled feature representation beyond face identification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adversarial disentanglement with grouped observations")
  Member "dual attention matching network for context aware feature sequence based person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond")
  Member "deep spatial feature reconstruction for partial person re identification alignment free approach":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "high order information matters learning relation and topology for occluded person re identification")
  Member "in defense of the triplet loss for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "camera style adaptation for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "weakly supervised discriminative feature learning with state information for person identification")

Question: Please tell me which class the source paper 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)