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: "adaptive correlation filters with long term and short term memory for object tracking"
  Abstract: Object tracking is challenging as target objects often undergo drastic appearance changes over time. Recently, adaptive correlation filters have been successfully applied to object tracking. However, tracking algorithms relying on highly adaptive correlation filters are prone to drift due to noisy updates. Moreover, as these algorithms do not maintain long-term memory of target appearance, they cannot recover from tracking failures caused by heavy occlusion or target disappearance in the camera view. In this paper, we propose to learn multiple adaptive correlation filters with both long-term and short-term memory of target appearance for robust object tracking. First, we learn a kernelized correlation filter with an aggressive learning rate for locating target objects precisely. We take into account the appropriate size of surrounding context and the feature representations. Second, we learn a correlation filter over a feature pyramid centered at the estimated target position for predicting scale changes. Third, we learn a complementary correlation filter with a conservative learning rate to maintain long-term memory of target appearance. We use the output responses of this long-term filter to determine if tracking failure occurs. In the case of tracking failures, we apply an incrementally learned detector to recover the target position in a sliding window fashion. Extensive experimental results on large-scale benchmark datasets demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods in terms of efficiency, accuracy, and robustness.

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"
    - "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"
    - "vital visual tracking via adversarial learning"

Connected through hyperedge HE-2 (induced by source paper "improving model drift for robust object tracking"):
  with members:
    - "deeper and wider siamese networks for real time visual tracking"
    - "siamese cascaded region proposal networks for real time visual tracking"
    - "distractor aware siamese networks for visual object tracking"
    - "unveiling the power of deep tracking"
    - "fast online object tracking and segmentation a unifying approach"
    - "siamrpn evolution of siamese visual tracking with very deep networks"
    - "dcfnet discriminant correlation filters network for visual tracking"
    - "end to end representation learning for correlation filter based tracking"

Connected through hyperedge HE-3 (induced by source paper "generic multiview visual tracking"):
  with members:
    - "siamese cascaded region proposal networks for real time visual tracking"
    - "a twofold siamese network for real time object tracking"
    - "lasot a high quality benchmark for large scale single object tracking"
    - "unveiling the power of deep tracking"
    - "dcfnet discriminant correlation filters network for visual tracking"
    - "learning spatial aware regressions for visual tracking"
    - "end to end flow correlation tracking with spatial temporal attention"
    - "a novel performance evaluation methodology for single target trackers"

Connected through hyperedge HE-4 (induced by source paper "tracking system of mine patrol robot for low illumination environment"):
  with members:
    - "end to end representation learning for correlation filter based tracking"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"
    - "large margin object tracking with circulant feature maps"
    - "learning background aware correlation filters for visual tracking"
    - "eco efficient convolution operators for tracking"
    - "beyond correlation filters learning continuous convolution operators for visual tracking"
    - "lime a method for low light image enhancement"
    - "a survey of appearance models in visual object tracking"

Connected through hyperedge HE-5 (induced by source paper "handcrafted and deep trackers recent visual object tracking approaches and trends"):
  with members:
    - "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"
    - "dual deep network for visual tracking"

Connected through hyperedge HE-6 (induced by source paper "handcrafted and deep trackers a review of recent object tracking approaches"):
  with members:
    - "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"
    - "large margin object tracking with circulant feature maps"

Connected through hyperedge HE-7 (induced by source paper "mobiface a novel dataset for mobile face tracking in the wild"):
  with members:
    - "vision meets drones a challenge"
    - "long term tracking in the wild a benchmark"
    - "meta tracker fast and robust online adaptation for visual object trackers"
    - "good features to correlate for visual tracking"
    - "vital visual tracking via adversarial learning"
    - "learning dynamic memory networks for object tracking"
    - "trackingnet a large scale dataset and benchmark for object tracking in the wild"
    - "dcfnet discriminant correlation filters network for visual tracking"

Connected through hyperedge HE-8 (induced by source paper "high performance visual tracking with circular and structural operators"):
  with members:
    - "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"
    - "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)