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 "efficient and deep person re identification using multi level similarity"):
  Abstract: Person Re-Identification (ReID) requires comparing two images of person captured under different conditions. Existing work based on neural networks often computes the similarity of feature maps from one single convolutional layer. In this work, we propose an efficient, end-to-end fully convolutional Siamese network that computes the similarities at multiple levels. We demonstrate that multi-level similarity can improve the accuracy considerably using low-complexity network structures in ReID problem. Specifically, first, we use several convolutional layers to extract the features of two input images. Then, we propose Convolution Similarity Network to compute the similarity score maps for the inputs. We use spatial transformer networks (STNs) to determine spatial attention. We propose to apply efficient depth-wise convolution to compute the similarity. The proposed Convolution Similarity Networks can be inserted into different convolutional layers to extract visual similarities at different levels. Furthermore, we use an improved ranking loss to further improve the performance. Our work is the first to propose to compute visual similarities at low, middle and high levels for ReID. With extensive experiments and analysis, we demonstrate that our system, compact yet effective, can achieve competitive results with much smaller model size and computational complexity.

Includes members:
  Member "beyond triplet loss a deep quadruplet network for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "memorizing comprehensively to learn adaptively unsupervised cross domain person re id with multi level memory")
  Member "person re identification by deep joint learning of multi loss classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond")
  Member "selective deep convolutional features for image retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "combination of multiple global descriptors for image retrieval")
  Member "learning deep context aware features over body and latent parts for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning shape representations for clothing variations in person re identification")
  Member "learning deep feature representations with domain guided dropout for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "building computationally efficient and well generalizing person re identification models with metric learning")
  Member "learning to hash with binary deep neural network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on deep hashing methods")
  Member "a multi task deep network for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cross spectrum dual subspace pairing for rgb infrared cross modality person re identification")
  Member "learning to rank in person re identification with metric ensembles":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "unsupervised person re identification by deep asymmetric metric embedding")

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)