human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "siamrpn evolution of siamese visual tracking with very deep networks"):
  with members:
    - "distractor aware siamese networks for visual object tracking"
    - "lasot a high quality benchmark for large scale single object tracking"
    - "unveiling the power of deep tracking"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "trackingnet a large scale dataset and benchmark for object tracking in the wild"
    - "dcfnet discriminant correlation filters network for visual tracking"
    - "an in depth analysis of visual tracking with siamese neural networks"
    - "end to end representation learning for correlation filter based tracking"

Connected through hyperedge HE-2 (induced by source paper "visual object tracking based on adaptive siamese and motion estimation network"):
  with members:
    - "a twofold siamese network for real time object tracking"
    - "meta tracker fast and robust online adaptation for visual object trackers"
    - "vital visual tracking via adversarial learning"
    - "end to end representation learning for correlation filter based tracking"
    - "crest convolutional residual learning for visual tracking"
    - "robust visual tracking via hierarchical convolutional features"
    - "sanet structure aware network for visual tracking"
    - "learning by tracking siamese cnn for robust target association"

Question: Please tell me which class the center node 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)