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: "an experimental survey on correlation filter based tracking"
  Abstract: Over these years, Correlation Filter-based Trackers (CFTs) have aroused increasing interests in the field of visual object tracking, and have achieved extremely compelling results in different competitions and benchmarks. In this paper, our goal is to review the developments of CFTs with extensive experimental results. 11 trackers are surveyed in our work, based on which a general framework is summarized. Furthermore, we investigate different training schemes for correlation filters, and also discuss various effective improvements that have been made recently. Comprehensive experiments have been conducted to evaluate the effectiveness and efficiency of the surveyed CFTs, and comparisons have been made with other competing trackers. The experimental results have shown that state-of-art performance, in terms of robustness, speed and accuracy, can be achieved by several recent CFTs, such as MUSTer and SAMF. We find that further improvements for correlation filter-based tracking can be made on estimating scales, applying part-based tracking strategy and cooperating with long-term tracking methods.

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 "robust visual tracking using multi frame multi feature joint modeling"):
  with members:
    - "parallel tracking and verifying a framework for real time and high accuracy visual tracking"
    - "a novel performance evaluation methodology for single target trackers"
    - "visual tracking via reliable memories"
    - "discriminative scale space tracking"
    - "tracking randomly moving objects on edge box proposals"
    - "learning multi domain convolutional neural networks for visual tracking"
    - "online object tracking with proposal selection"
    - "a survey of appearance models in visual object tracking"

Connected through hyperedge HE-3 (induced by source paper "material based object tracking in hyperspectral videos benchmark and algorithms"):
  with members:
    - "context aware deep feature compression for high speed visual tracking"
    - "object tracking in hyperspectral videos with convolutional features and kernelized correlation filter"
    - "end to end representation learning for correlation filter based tracking"
    - "learning spatial aware regressions for visual tracking"
    - "tracking in aerial hyperspectral videos using deep kernelized correlation filters"
    - "learning background aware correlation filters for visual tracking"
    - "eco efficient convolution operators for tracking"
    - "geometry informed material recognition"

Connected through hyperedge HE-4 (induced by source paper "part based tracking by sampling"):
  with members:
    - "learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking"
    - "superpixels an evaluation of the state of the art"
    - "visual object tracking the initialisation problem"
    - "learning spatial aware regressions for visual tracking"
    - "a novel performance evaluation methodology for single target trackers"
    - "geometric hypergraph learning for visual tracking"
    - "beyond correlation filters learning continuous convolution operators for visual tracking"
    - "learning spatially regularized correlation filters for visual tracking"

Connected through hyperedge HE-5 (induced by source paper "long term correlation tracking using multi layer hybrid features in sparse and dense environments"):
  with members:
    - "learning multi domain convolutional neural networks for visual tracking"
    - "high speed tracking with kernelized correlation filters"
    - "online tracking by learning discriminative saliency map with convolutional neural network"
    - "very deep convolutional networks for large scale image recognition"
    - "matconvnet convolutional neural networks for matlab"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "vision based real time aerial object localization and tracking for uav sensing system"):
  with members:
    - "salient object detection a survey"
    - "end to end representation learning for correlation filter based tracking"
    - "real time visual tracking promoting the robustness of correlation filter learning"
    - "high speed tracking with kernelized correlation filters"
    - "online tracking by learning discriminative saliency map with convolutional neural network"

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)