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 "unsupervised deep tracking"):
  Abstract: We propose an unsupervised visual tracking method in this paper. Different from existing approaches using extensive annotated data for supervised learning, our CNN model is trained on large-scale unlabeled videos in an unsupervised manner. Our motivation is that a robust tracker should be effective in both the forward and backward predictions (i.e., the tracker can forward localize the target object in successive frames and backtrace to its initial position in the first frame). We build our framework on a Siamese correlation filter network, which is trained using unlabeled raw videos. Meanwhile, we propose a multiple-frame validation method and a cost-sensitive loss to facilitate unsupervised learning. Without bells and whistles, the proposed unsupervised tracker achieves the baseline accuracy of fully supervised trackers, which require complete and accurate labels during training. Furthermore, unsupervised framework exhibits a potential in leveraging unlabeled or weakly labeled data to further improve the tracking accuracy.

Includes members:
  Member "real time mdnet":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "m 5 l multi modal multi margin metric learning for rgbt tracking")
  Member "long term tracking in the wild a benchmark":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "a twofold siamese network for real time object tracking":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "spark spatial aware online incremental attack against visual tracking")
  Member "end to end active object tracking and its real world deployment via reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "pose assisted multi camera collaboration for active object tracking")
  Member "deep attentive tracking via reciprocative learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a review of tracking prediction and decision making methods for autonomous driving")
  Member "vital visual tracking via adversarial learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "siamese box adaptive network for visual tracking")
  Member "learning dynamic memory networks for object tracking":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "object adaptive lstm network for real time visual tracking with adversarial data augmentation")
  Member "trackingnet a large scale dataset and benchmark for object tracking in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "spark spatial aware online incremental attack against visual tracking")

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)