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: "robust visual tracking revisited from correlation filter to template matching"
  Abstract: In this paper, we propose a novel matching based tracker by investigating the relationship between template matching and the recent popular correlation filter based trackers (CFTs). Compared to the correlation operation in CFTs, a sophisticated similarity metric termed mutual buddies similarity is proposed to exploit the relationship of multiple reciprocal nearest neighbors for target matching. By doing so, our tracker obtains powerful discriminative ability on distinguishing target and background as demonstrated by both empirical and theoretical analyses. Besides, instead of utilizing single template with the improper updating scheme in CFTs, we design a novel online template updating strategy named memory, which aims to select a certain amount of representative and reliable tracking results in history to construct the current stable and expressive template set. This scheme is beneficial for the proposed tracker to comprehensively understand the target appearance variations, recall some stable results. Both qualitative and quantitative evaluations on two benchmarks suggest that the proposed tracking method performs favorably against some recently developed CFTs and other competitive trackers.

Connected through hyperedge HE-1 (induced by source paper "real time correlation tracking via joint model compression and transfer"):
  with members:
    - "unsupervised deep tracking"
    - "real time mdnet"
    - "distractor aware siamese networks for visual object tracking"
    - "a twofold siamese network for real time object tracking"
    - "good features to correlate for visual tracking"
    - "high speed tracking with multi kernel correlation filters"
    - "deep attentive tracking via reciprocative learning"
    - "unveiling the power of deep tracking"

Connected through hyperedge HE-2 (induced by source paper "learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking"):
  with members:
    - "distractor aware siamese networks for visual object tracking"
    - "good features to correlate for visual tracking"
    - "unveiling the power of deep tracking"
    - "end to end representation learning for correlation filter based tracking"
    - "learning spatial aware regressions for visual tracking"
    - "crest convolutional residual learning for visual tracking"
    - "learning background aware correlation filters for visual tracking"
    - "eco efficient convolution operators for tracking"

Connected through hyperedge HE-3 (induced by source paper "unsupervised online video object segmentation with motion property understanding"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "fast and accurate online video object segmentation via tracking parts"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "efficient video object segmentation via network modulation"
    - "lucid data dreaming for video object segmentation"
    - "online adaptation of convolutional neural networks for video object segmentation"
    - "mask r cnn"
    - "learning motion patterns in videos"

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)