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 "camera adversarial transfer for unsupervised person re identification"):
  Abstract: Unsupervised person re-identification (Re-ID) methods consist of training with a carefully labeled source dataset, followed by generalization to an unlabeled target dataset, i.e. person-identity information is unavailable. Inspired by domain adaptation techniques, these methods avoid a costly, tedious and often unaffordable labeling process. This paper investigates the use of camera-index information, namely which camera captured which image, for unsupervised person Re-ID. More precisely, inspired by domain adaptation adversarial approaches, we develop an adversarial framework in which the output of the feature extractor should be useful for person Re-ID and in the same time should fool a camera discriminator. We refer to the proposed method as camera adversarial transfer (CAT). We evaluate adversarial variants and, alongside, the camera robustness achieved for each variant. We report cross-dataset ReID performance and we compare the variants of our method with several state-of-the-art methods, thus showing the interest of exploiting camera-index information within an adversarial framework for the unsupervised person Re-ID.

Includes members:
  Member "unsupervised cross dataset person re identification by transfer learning of spatial temporal patterns":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unsupervised multiple person tracking using autoencoder based lifted multicuts")
  Member "transferable joint attribute identity deep learning for unsupervised person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "weakly supervised discriminative feature learning with state information for person identification")
  Member "unsupervised person re identification by deep learning tracklet association":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "weakly supervised discriminative feature learning with state information for person identification")
  Member "camera style adaptation for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "weakly supervised discriminative feature learning with state information for person identification")
  Member "person re identification by deep joint learning of multi loss classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond")
  Member "dynamic label graph matching for unsupervised video re identification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "intra camera supervised person re identification")
  Member "adversarial discriminative domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice")
  Member "cross view asymmetric metric learning for unsupervised person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "building computationally efficient and well generalizing person re identification models with metric learning")

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)