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: "large margin structured convolution operator for thermal infrared object tracking"
  Abstract: Compared with visible object tracking, thermal infrared (TIR) object tracking can track an arbitrary target in total darkness since it cannot be influenced by illumination variations. However, there are many unwanted attributes that constrain the potentials of TIR tracking, such as the absence of visual color patterns and low resolutions. Recently, structured output support vector machine (SOSVM) and discriminative correlation filter (DCF) have been successfully applied to visible object tracking, respectively. Motivated by these, in this paper, we propose a large margin structured convolution operator (LMSCO) to achieve efficient TIR object tracking. To improve the tracking performance, we employ the spatial regularization and implicit interpolation to obtain continuous deep feature maps, including deep appearance features and deep motion features, of the TIR targets. Finally, a collaborative optimization strategy is exploited to significantly update the operators. Our approach not only inherits the advantage of the strong discriminative capability of SOSVM but also achieves accurate and robust tracking with higher-dimensional features and more dense samples. To the best of our knowledge, we are the first to incorporate the advantages of DCF and SOSVM for TIR object tracking. Comprehensive evaluations on two thermal infrared tracking benchmarks, i.e. VOT-TIR2015 and VOT-TIR2016, clearly demonstrate that our LMSCO tracker achieves impressive results and outperforms most state-of-the-art trackers in terms of accuracy and robustness with sufficient frame rate.

Connected through hyperedge HE-1 (induced by source paper "siamese attentional keypoint network for high performance visual tracking"):
  with members:
    - "learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking"
    - "learning reinforced attentional representation for end to end visual tracking"
    - "learning cascaded siamese networks for high performance visual tracking"
    - "deeper and wider siamese networks for real time visual tracking"
    - "cornernet detecting objects as paired keypoints"
    - "distractor aware siamese networks for visual object tracking"
    - "efficient multi level correlating for visual tracking"
    - "high performance visual tracking with circular and structural operators"

Connected through hyperedge HE-2 (induced by source paper "multi task driven feature models for thermal infrared tracking"):
  with members:
    - "ptb tir a thermal infrared pedestrian tracking benchmark"
    - "learning deep multi level similarity for thermal infrared object tracking"
    - "synthetic data generation for end to end thermal infrared tracking"
    - "deeper and wider siamese networks for real time visual tracking"
    - "target aware deep tracking"
    - "a twofold siamese network for real time object tracking"
    - "vital visual tracking via adversarial learning"
    - "learning spatial temporal regularized correlation filters for visual tracking"

Connected through hyperedge HE-3 (induced by source paper "learning cascaded siamese networks for high performance visual tracking"):
  with members:
    - "siamese attentional keypoint network for high performance visual tracking"
    - "efficient multi level correlating for visual tracking"
    - "high performance visual tracking with circular and structural operators"
    - "a complementary tracking model with multiple features"
    - "end to end representation learning for correlation filter based tracking"
    - "eco efficient convolution operators for tracking"
    - "siamese instance search for tracking"
    - "fully convolutional siamese networks for object tracking"

Connected through hyperedge HE-4 (induced by source paper "learning deep multi level similarity for thermal infrared object tracking"):
  with members:
    - "a twofold siamese network for real time object tracking"
    - "learning spatial temporal regularized correlation filters for visual tracking"
    - "squeeze and excitation networks"
    - "end to end flow correlation tracking with spatial temporal attention"
    - "eco efficient convolution operators for tracking"
    - "beyond correlation filters learning continuous convolution operators for visual tracking"
    - "image to image translation with conditional adversarial networks"
    - "high speed tracking with kernelized correlation filters"

Connected through hyperedge HE-5 (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"
    - "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"
    - "eco efficient convolution operators for tracking"

Connected through hyperedge HE-6 (induced by source paper "efficient multi level correlating for visual tracking"):
  with members:
    - "high performance visual tracking with circular and structural operators"
    - "a complementary tracking model with multiple features"
    - "good features to correlate for visual tracking"
    - "end to end representation learning for correlation filter based tracking"
    - "large margin object tracking with circulant feature maps"
    - "crest convolutional residual learning for visual tracking"
    - "learning background aware correlation filters for visual tracking"
    - "eco efficient convolution operators for 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)