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: "learning support correlation filters for visual tracking"
  Abstract: Sampling and budgeting training examples are two essential factors in tracking algorithms based on support vector machines (SVMs) as a trade-off between accuracy and efficiency. Recently, the circulant matrix formed by dense sampling of translated image patches has been utilized in correlation filters for fast tracking. In this paper, we derive an equivalent formulation of a SVM model with circulant matrix expression and present an efficient alternating optimization method for visual tracking. We incorporate the discrete Fourier transform with the proposed alternating optimization process, and pose the tracking problem as an iterative learning of support correlation filters (SCFs) which find the global optimal solution with real-time performance. For a given circulant data matrix with n^2 samples of size n*n, the computational complexity of the proposed algorithm is O(n^2*logn) whereas that of the standard SVM-based approaches is at least O(n^4). In addition, we extend the SCF-based tracking algorithm with multi-channel features, kernel functions, and scale-adaptive approaches to further improve the tracking performance. Experimental results on a large benchmark dataset show that the proposed SCF-based algorithms perform favorably against the state-of-the-art tracking methods in terms of accuracy and speed.

Connected through hyperedge HE-1 (induced by source paper "see more know more unsupervised video object segmentation with co attention siamese networks"):
  with members:
    - "deep visual attention prediction"
    - "fast and accurate online video object segmentation via tracking parts"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "unsupervised video object segmentation using motion saliency guided spatio temporal propagation"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "efficient video object segmentation via network modulation"
    - "videomatch matching based video object segmentation"
    - "pairwise body part attention for recognizing human object interactions"

Connected through hyperedge HE-2 (induced by source paper "joint group feature selection and discriminative filter learning for robust visual object tracking"):
  with members:
    - "learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking"
    - "deep visual attention prediction"
    - "distractor aware siamese networks for visual object tracking"
    - "meta tracker fast and robust online adaptation for visual object trackers"
    - "good features to correlate for visual tracking"
    - "high speed tracking with multi kernel correlation filters"
    - "unveiling the power of deep tracking"
    - "vital visual tracking via adversarial learning"

Connected through hyperedge HE-3 (induced by source paper "deeper and wider siamese networks for real time visual tracking"):
  with members:
    - "distractor aware siamese networks for visual object tracking"
    - "a twofold siamese network for real time object tracking"
    - "good features to correlate for visual tracking"
    - "unveiling the power of deep tracking"
    - "vital visual tracking via adversarial learning"
    - "end to end representation learning for correlation filter based tracking"
    - "learning spatial aware regressions for visual tracking"
    - "youtube boundingboxes a large high precision human annotated data set for object detection in video"

Connected through hyperedge HE-4 (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"
    - "robust visual tracking revisited from correlation filter to template matching"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "remove cosine window from correlation filter based visual trackers when and how"):
  with members:
    - "learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking"
    - "context aware deep feature compression for high speed visual tracking"
    - "a twofold siamese network for real time object tracking"
    - "unveiling the power of deep tracking"
    - "correlation tracking via joint discrimination and reliability learning"
    - "vital visual tracking via adversarial learning"
    - "integrating boundary and center correlation filters for visual tracking with aspect ratio variation"
    - "end to end representation learning for correlation filter based tracking"

Connected through hyperedge HE-6 (induced by source paper "an accelerated correlation filter tracker"):
  with members:
    - "joint group feature selection and discriminative filter learning for robust visual object tracking"
    - "learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking"
    - "deep visual attention prediction"
    - "meta tracker fast and robust online adaptation for visual object trackers"
    - "video salient object detection via fully convolutional networks"
    - "learning spatial temporal regularized correlation filters for visual tracking"
    - "end to end representation learning for correlation filter based tracking"
    - "crest convolutional residual learning for visual tracking"

Connected through hyperedge HE-7 (induced by source paper "adaptive distraction context aware tracking based on correlation filter"):
  with members:
    - "an accelerated correlation filter tracker"
    - "joint group feature selection and discriminative filter learning for robust visual object tracking"
    - "high speed tracking with kernelized correlation filters"

Connected through hyperedge HE-8 (induced by source paper "high performance visual tracking with circular and structural operators"):
  with members:
    - "adaptive correlation filters with long term and short term memory for object tracking"
    - "large margin structured convolution operator for thermal infrared object tracking"
    - "a complementary tracking model with multiple features"
    - "good features to correlate for visual tracking"
    - "learning spatial aware regressions for visual tracking"
    - "large margin object tracking with circulant feature maps"
    - "learning background aware correlation filters for visual tracking"
    - "a novel performance evaluation methodology for single target trackers"

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)