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 "vmrfanet view specific multi receptive field attention network for person re identification"):
  Abstract: Person re-identification (re-ID) aims to retrieve the same person across different cameras. In practice, it still remains a challenging task due to background clutter, variations on body poses and view conditions, inaccurate bounding box detection, etc. To tackle these issues, in this paper, we propose a novel multi-receptive field attention (MRFA) module that utilizes filters of various sizes to help network focusing on informative pixels. Besides, we present a view-specific mechanism that guides attention module to handle the variation of view conditions. Moreover, we introduce a Gaussian horizontal random cropping/padding method which further improves the robustness of our proposed network. Comprehensive experiments demonstrate the effectiveness of each component. Our method achieves 95.5% / 88.1% in rank-1 / mAP on Market-1501, 88.9% / 80.0% on DukeMTMC-reID, 81.1% / 78.8% on CUHK03 labeled dataset and 78.9% / 75.3% on CUHK03 detected dataset, outperforming current state-of-the-art methods.

Includes members:
  Member "learning discriminative features with multiple granularities for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "building computationally efficient and well generalizing person re identification models with metric learning")
  Member "horizontal pyramid matching for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "building computationally efficient and well generalizing person re identification models with metric learning")
  Member "in defense of the triplet loss for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "random erasing data augmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on feature normalization and data augmentation")
  Member "alignedreid surpassing human level performance in 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 "beyond part models person retrieval with refined part pooling and a strong convolutional baseline":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond")
  Member "pedestrian alignment network for large scale person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond")
  Member "svdnet for pedestrian retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond")

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)