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 "siamese learning visual tracking a survey"):
  Abstract: The aim of this survey is an attempt to review the kind of machine learning and stochastic techniques and the ways existing work currently uses machine learning and stochastic methods for the challenging problem of visual tracking. It is not intended to study the whole tracking literature of the last decades as this seems impossible by the incredible vast number of published papers. This first draft version of the article focuses very targeted on recent literature that suggests Siamese networks for the learning of tracking. This approach promise a step forward in terms of robustness, accuracy and computational efficiency. For example, the representative tracker SINT performs currently best on the popular OTB-2013 benchmark with AuC/IoU/prec. 65.5/62.5/84.8 % for the one-pass experiment (OPE). The CVPR'17 work CVNet by the Oxford group shows the approach's large potential of HW/SW co-design with network memory needs around 600 kB and frame-rates of 75 fps and beyond. Before a detailed description of this approach is given, the article recaps the definition of tracking, the current state-of-the-art view on designing algorithms and the state-of-the-art of trackers by summarising insights from existing literature. In future, the article will be extended by the review of two alternative approaches, the one using very general recurrent networks such as the Long Shortterm Memory (LSTM) networks and the other most obvious approach of applying sole convolutional networks (CNN), the earliest approach since the idea of deep learning tracking appeared at NIPS'13.

Includes members:
  Member "fast and accurate online video object segmentation via tracking parts":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "spark spatial aware online incremental attack against visual tracking")
  Member "good features to correlate for visual tracking":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "siamese attentional keypoint network for high performance visual tracking")
  Member "trackingnet a large scale dataset and benchmark for object tracking in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "spark spatial aware online incremental attack against visual tracking")
  Member "end to end representation learning for correlation filter based tracking":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "siamese attentional keypoint network for high performance visual tracking")
  Member "focal loss for dense object detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "hierarchical attentive recurrent tracking":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "scalor generative world models with scalable object representations")
  Member "multi task self supervised visual learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")

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)