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: "a complementary tracking model with multiple features"
  Abstract: Discriminative Correlation Filters (DCF)-based tracking algorithms exploiting conventional handcrafted features have achieved impressive results both in terms of accuracy and robustness. Template handcrafted features have shown excellent performance, but they perform poorly when the appearance of target changes rapidly such as fast motions and fast deformations. In contrast, statistical handcrafted features are insensitive to fast states changes, but they yield inferior performance in the scenarios of illumination variations and background clutters. In this work, to achieve an efficient tracking performance, we propose a novel visual tracking algorithm, named MFCMT, based on a complementary ensemble model with multiple features, including Histogram of Oriented Gradients (HOGs), Color Names (CNs) and Color Histograms (CHs). Additionally, to improve tracking results and prevent targets drift, we introduce an effective fusion method by exploiting relative entropy to coalesce all basic response maps and get an optimal response. Furthermore, we suggest a simple but efficient update strategy to boost tracking performance. Comprehensive evaluations are conducted on two tracking benchmarks demonstrate and the experimental results demonstrate that our method is competitive with numerous state-of-the-art trackers. Our tracker achieves impressive performance with faster speed on these benchmarks.

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"
    - "large margin structured convolution operator for thermal infrared object tracking"
    - "distractor aware siamese networks for visual object tracking"
    - "efficient multi level correlating for visual tracking"

Connected through hyperedge HE-2 (induced by source paper "learning cascaded siamese networks for high performance visual tracking"):
  with members:
    - "siamese attentional keypoint network for high performance visual tracking"
    - "large margin structured convolution operator for thermal infrared object tracking"
    - "efficient multi level correlating for visual tracking"
    - "high performance visual tracking with circular and structural operators"
    - "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-3 (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"
    - "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-4 (induced by source paper "efficient multi level correlating for visual tracking"):
  with members:
    - "large margin structured convolution operator for thermal infrared object tracking"
    - "high performance visual tracking with circular and structural operators"
    - "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)