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: "beyond standard benchmarks parameterizing performance evaluation in visual object tracking"
  Abstract: Object-to-camera motion produces a variety of apparent motion patterns that significantly affect performance of short-term visual trackers. Despite being crucial for designing robust trackers, their influence is poorly explored in standard benchmarks due to weakly defined, biased and overlapping attribute annotations. In this paper we propose to go beyond pre-recorded benchmarks with post-hoc annotations by presenting an approach that utilizes omnidirectional videos to generate realistic, consistently annotated, short-term tracking scenarios with exactly parameterized motion patterns. We have created an evaluation system, constructed a fully annotated dataset of omnidirectional videos and the generators for typical motion patterns. We provide an in-depth analysis of major tracking paradigms which is complementary to the standard benchmarks and confirms the expressiveness of our evaluation approach.

Connected through hyperedge HE-1 (induced by source paper "a review of visual trackers and analysis of its application to mobile robot"):
  with members:
    - "long term tracking in the wild a benchmark"
    - "context aware deep feature compression for high speed visual tracking"
    - "adaptive correlation filters with long term and short term memory for object tracking"
    - "joint representation and truncated inference learning for correlation filter based tracking"
    - "a twofold siamese network for real time object tracking"
    - "meta tracker fast and robust online adaptation for visual object trackers"
    - "good features to correlate for visual tracking"
    - "unveiling the power of deep tracking"

Connected through hyperedge HE-2 (induced by source paper "performance evaluation methodology for long term visual object tracking"):
  with members:
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "long term tracking in the wild a benchmark"
    - "now you see me evaluating performance in long term visual tracking"
    - "trackingnet a large scale dataset and benchmark for object tracking in the wild"
    - "tracking for half an hour"
    - "parallel tracking and verifying a framework for real time and high accuracy visual tracking"
    - "crest convolutional residual learning for visual tracking"
    - "long term visual object tracking benchmark"

Connected through hyperedge HE-3 (induced by source paper "cylks unsupervised cycle lucas kanade network for landmark tracking"):
  with members:
    - "end to end representation learning for correlation filter based tracking"
    - "deep lk for efficient adaptive object tracking"
    - "detect to track and track to detect"
    - "parallel tracking and verifying a framework for real time and high accuracy visual tracking"
    - "eco efficient convolution operators for tracking"
    - "photometric bundle adjustment for vision based slam"
    - "beyond correlation filters learning continuous convolution operators for visual tracking"
    - "inverse compositional spatial transformer networks"

Connected through hyperedge HE-4 (induced by source paper "now you see me evaluating performance in long term visual tracking"):
  with members:
    - "tracking for half an hour"
    - "parallel tracking and verifying a framework for real time and high accuracy visual tracking"
    - "crest convolutional residual learning for visual tracking"
    - "long term visual object tracking benchmark"
    - "need for speed a benchmark for higher frame rate object tracking"
    - "a novel performance evaluation methodology for single target trackers"
    - "learning multi domain convolutional neural networks for visual tracking"
    - "motchallenge 2015 towards a benchmark for multi target tracking"

Connected through hyperedge HE-5 (induced by source paper "long term visual object tracking benchmark"):
  with members:
    - "long term tracking in the wild a benchmark"
    - "now you see me evaluating performance in long term visual tracking"
    - "crest convolutional residual learning for visual tracking"
    - "learning background aware correlation filters for visual tracking"
    - "need for speed a benchmark for higher frame rate object tracking"
    - "eco efficient convolution operators for tracking"
    - "fully convolutional siamese networks for object tracking"
    - "beyond correlation filters learning continuous convolution operators for visual tracking"

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)