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 "fully convolutional siamese networks for object tracking"):
  Abstract: The problem of arbitrary object tracking has traditionally been tackled by learning a model of the object's appearance exclusively online, using as sole training data the video itself. Despite the success of these methods, their online-only approach inherently limits the richness of the model they can learn. Recently, several attempts have been made to exploit the expressive power of deep convolutional networks. However, when the object to track is not known beforehand, it is necessary to perform Stochastic Gradient Descent online to adapt the weights of the network, severely compromising the speed of the system. In this paper we equip a basic tracking algorithm with a novel fully-convolutional Siamese network trained end-to-end on the ILSVRC15 dataset for object detection in video. Our tracker operates at frame-rates beyond real-time and, despite its extreme simplicity, achieves state-of-the-art performance in multiple benchmarks.

Includes members:
  Member "siamese instance search for tracking":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "siamese attentional keypoint network for high performance visual tracking")
  Member "learning to track at 100 fps with deep regression networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "object adaptive lstm network for real time visual tracking with adversarial data augmentation")
  Member "once for all a two flow convolutional neural network for visual tracking":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generic multiview visual tracking")
  Member "learning spatially regularized correlation filters for visual tracking":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "siamese attentional keypoint network for high performance visual tracking")
  Member "transferring rich feature hierarchies for robust visual tracking":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "keyfilter aware real time uav object tracking")
  Member "tracking randomly moving objects on edge box proposals":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "quadruplet network with one shot learning for fast visual object tracking")
  Member "first step toward model free anonymous object tracking with recurrent neural networks":
    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 "learning multi domain convolutional neural networks for visual tracking":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "siamese attentional keypoint network for high performance 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)