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 "got 10k a large high diversity benchmark for generic object tracking in the wild"):
  Abstract: We introduce here a large tracking database that offers an unprecedentedly wide coverage of common moving objects in the wild, called GOT-10k. Specifically, GOT-10k is built upon the backbone of WordNet structure and it populates the majority of over 560 classes of moving objects and 87 motion patterns, magnitudes wider than the most recent similar-scale counterparts. The contributions of this paper are summarized in the following: (1) GOT-10k offers over 10,000 video segments with more than 1.5 million manually labeled bounding boxes, enabling unified training and stable evaluation of deep trackers. (2) GOT-10k is by far the first video trajectory dataset that uses the semantic hierarchy of WordNet to guide class population. (3) For the first time, GOT-10k introduces the one-shot protocol for tracker evaluation, where the training and test classes are zero-overlapped. The protocol avoids biased evaluation results towards familiar objects and it promotes generalization in tracker development. (4) We conduct extensive tracking experiments with 39 typical tracking algorithms on GOT-10k and analyze their results in this paper. (5) Finally, we develop a comprehensive platform for the tracking community that offers full-featured evaluation toolkits, an online evaluation server, and a responsive leaderboard. The annotations of GOT-10k's test data are kept private to avoid tuning parameters on it. The database, toolkits, evaluation server and baseline results are available at this http URL

Includes members:
  Member "ptb tir a thermal infrared pedestrian tracking benchmark":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi task driven feature models for thermal infrared tracking")
  Member "class balanced loss based on effective number of samples":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "identifying and compensating for feature deviation in imbalanced deep learning")
  Member "deeper and wider siamese networks for real time visual tracking":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "siamese attentional keypoint network for high performance visual tracking")
  Member "real time mdnet":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "fast and accurate online video object segmentation via tracking parts":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (induced by source paper "spark spatial aware online incremental attack against visual tracking")
  Member "meta tracker fast and robust online adaptation for visual object trackers":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "aol adaptive online learning for human trajectory prediction in dynamic video scenes")

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)