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 based on res2net network"):
  Abstract: Person re-identification (re-ID) has been gaining in popularity in the research community owing to its numerous applications and growing importance in the surveillance industry. Person re-ID remains challenging due to significant intra-class variations across different cameras. In this paper, we propose a multi-task network that simultaneously computes the identification loss and verification loss. Given a pair of input images, the network predicts the identities of the two input images and whether they belong to the same identity. In order to obtain deeper feature information of pedestrians, we propose to use the latest Res2Net network for feature extraction. Experiments on several large-scale person re-ID benchmark datasets demonstrate the accuracy of our approach. For example, rank-1 accuracies are 82.67% (+0.51) and 92.93% (+0.21) for the DukeMTMC and Market-1501 datasets, respectively. The proposed method shows encouraging improvements compared with state-of-the-art methods.

Includes members:
  Member "res2net a new multi scale backbone architecture":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "highly efficient salient object detection with 100k parameters")
  Member "deep representation learning with part loss for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning shape representations for clothing variations in person re identification")
  Member "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "searching for winograd aware quantized networks")
  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 "big little net an efficient multi scale feature representation for visual and speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "slimconv reducing channel redundancy in convolutional neural networks by weights flipping")
  Member "random erasing data augmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on feature normalization and data augmentation")
  Member "a discriminatively learned cnn embedding for person reidentification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "magnifiernet towards semantic regularization and fusion for person re identification")
  Member "person re identification by deep joint learning of multi loss classification":
    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)