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 "deep similarity metric learning for real time pedestrian tracking"):
  Abstract: Tracking by detection is a common approach to solving the Multiple Object Tracking problem. In this paper we show how learning a deep similarity metric can improve three key aspects of pedestrian tracking on a multiple object tracking benchmark. We train a convolutional neural network to learn an embedding function in a Siamese configuration on a large person re-identification dataset. The offline-trained embedding network is integrated in to the tracking formulation to improve performance while retaining real-time performance. The proposed tracker stores appearance metrics while detections are strong, using this appearance information to: prevent ID switches, associate tracklets through occlusion, and propose new detections where detector confidence is low. This method achieves competitive results in evaluation, especially among online, real-time approaches. We present an ablative study showing the impact of each of the three uses of our deep appearance metric.

Includes members:
  Member "real time multiple people tracking with deeply learned candidate selection and person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "panda a gigapixel level human centric video dataset")
  Member "in defense of the triplet loss for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "real time multiple object tracking a study on the importance of speed":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "densecavoid real time navigation in dense crowds using anticipatory behaviors")
  Member "sot for mot":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "graph neural based end to end data association framework for online multiple object tracking")
  Member "simple online and realtime tracking with a deep association metric":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "spatiotemporal relationship reasoning for pedestrian intent prediction")
  Member "end to end comparative attention networks 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 "mot16 a benchmark for multi object tracking":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "learning by tracking siamese cnn for robust target association":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multiple object tracking by flowing and fusing")

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)