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: "geometric hypergraph learning for visual tracking"
  Abstract: Graph based representation is widely used in visual tracking field by finding correct correspondences between target parts in consecutive frames. However, most graph based trackers consider pairwise geometric relations between local parts. They do not make full use of the target's intrinsic structure, thereby making the representation easily disturbed by errors in pairwise affinities when large deformation and occlusion occur. In this paper, we propose a geometric hypergraph learning based tracking method, which fully exploits high-order geometric relations among multiple correspondences of parts in consecutive frames. Then visual tracking is formulated as the mode-seeking problem on the hypergraph in which vertices represent correspondence hypotheses and hyperedges describe high-order geometric relations. Besides, a confidence-aware sampling method is developed to select representative vertices and hyperedges to construct the geometric hypergraph for more robustness and scalability. The experiments are carried out on two challenging datasets (VOT2014 and Deform-SOT) to demonstrate that the proposed method performs favorable against other existing trackers.

Connected through hyperedge HE-1 (induced by source paper "sg fcn a motion and memory based deep learning model for video saliency detection"):
  with members:
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "exploring models and data for remote sensing image caption generation"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "exploiting inter image similarity and ensemble of extreme learners for fixation prediction using deep features"
    - "deepsaliency multi task deep neural network model for salient object detection"
    - "adaptive compressive tracking via online vector boosting feature selection"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-2 (induced by source paper "handcrafted and deep trackers recent visual object tracking approaches and trends"):
  with members:
    - "adaptive correlation filters with long term and short term memory for object tracking"
    - "discriminative correlation filter tracker with channel and spatial reliability"
    - "vital visual tracking via adversarial learning"
    - "learning spatial temporal regularized correlation filters for visual tracking"
    - "dcfnet discriminant correlation filters network for visual tracking"
    - "end to end representation learning for correlation filter based tracking"
    - "learning policies for adaptive tracking with deep feature cascades"
    - "tracking the trackers an analysis of the state of the art in multiple object tracking"

Connected through hyperedge HE-3 (induced by source paper "handcrafted and deep trackers a review of recent object tracking approaches"):
  with members:
    - "adaptive correlation filters with long term and short term memory for object tracking"
    - "vital visual tracking via adversarial learning"
    - "dcfnet discriminant correlation filters network for visual tracking"
    - "end to end representation learning for correlation filter based tracking"
    - "real time visual tracking by deep reinforced decision making"
    - "learning policies for adaptive tracking with deep feature cascades"
    - "tracking the trackers an analysis of the state of the art in multiple object tracking"
    - "dual deep network for visual tracking"

Connected through hyperedge HE-4 (induced by source paper "large margin structured convolution operator for thermal infrared object tracking"):
  with members:
    - "large margin object tracking with circulant feature maps"
    - "a novel performance evaluation methodology for single target trackers"
    - "eco efficient convolution operators for tracking"
    - "learning support correlation filters for visual tracking"
    - "beyond correlation filters learning continuous convolution operators for visual tracking"
    - "deep motion features for visual tracking"
    - "discriminative scale space tracking"
    - "learning spatially regularized correlation filters for visual tracking"

Connected through hyperedge HE-5 (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"
    - "beyond correlation filters learning continuous convolution operators for visual tracking"
    - "learning spatially regularized correlation filters for visual tracking"
    - "an experimental survey on correlation filter based tracking"

Connected through hyperedge HE-6 (induced by source paper "unified graph based multi cue feature fusion for robust visual tracking"):
  with members:
    - "good features to correlate for visual tracking"
    - "a novel performance evaluation methodology for single target trackers"

Connected through hyperedge HE-7 (induced by source paper "visual object tracking the initialisation problem"):
  with members:
    - "learning multi domain convolutional neural networks for visual tracking"

Connected through hyperedge HE-8 (induced by source paper "fclt a fully correlational long term tracker"):
  with members:
    - "good features to correlate for visual tracking"
    - "learning policies for adaptive tracking with deep feature cascades"
    - "dual deep network for visual tracking"
    - "parallel tracking and verifying a framework for real time and high accuracy visual tracking"
    - "a novel performance evaluation methodology for single target trackers"
    - "fully convolutional siamese networks for object tracking"
    - "beyond correlation filters learning continuous convolution operators for visual tracking"
    - "discriminative scale space 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)