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 "ranking and classification driven feature learning for person re_identification"):
  Abstract: Person re-identification has attracted many researchers' attention for its wide application, but it is still a very challenging task because only part of the image information can be used for personnel matching. Most of current methods uses CNN to learn to embeddings that can capture semantic similarity information among data points. Many of the state-of-the-arts methods use complex network structures with multiple branches that fuse multiple features while training or testing, using classification loss, Triplet loss or a combination of the two as loss function. However, the method that using Triplet loss as loss function converges slowly, and the method in which pull features of the same class as close as possible in features space leads to poor feature stability. This paper will combine the ranking motivated structured loss, proposed a new metric learning loss function that make the features of the same class are sparsely distributed into the range of small hyperspheres and the features of different classes are uniformly distributed at a clearly angle. And adopted a new single-branch network structure that only using global feature can also get great performance. The validity of our method is verified on the Market1501 and DukeMTMC-ReID person re-identification datasets. Finally acquires 90.9% rank-1 accuracy and 80.8% mAP on DukeMTMC-reID, 95.3% rank-1 accuracy and 88.7% mAP on Market1501. Codes and models are available in Github.this https URL.

Includes members:
  Member "deep constrained dominant sets for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "constrained dominant sets and its applications in computer vision")
  Member "ranked list loss for deep metric learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "revisiting training strategies and generalization performance in deep metric learning")
  Member "omni scale feature learning for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "building computationally efficient and well generalizing person re identification models with metric learning")
  Member "bag of tricks and a strong baseline for deep person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "building computationally efficient and well generalizing person re identification models with metric learning")
  Member "harmonious attention network for 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 "maskreid a mask based deep ranking neural network for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "disassembling the dataset a camera alignment mechanism for multiple tasks in person re identification")
  Member "scpnet spatial channel parallelism network for joint holistic and partial person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "disassembling the dataset a camera alignment mechanism for multiple tasks in person re identification")
  Member "dual attention matching network for context aware feature sequence based person re identification":
    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)