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: "integrating boundary and center correlation filters for visual tracking with aspect ratio variation"
  Abstract: The aspect ratio variation frequently appears in visual tracking and has a severe influence on performance. Although many correlation filter (CF)-based trackers have also been suggested for scale adaptive tracking, few studies have been given to handle the aspect ratio variation for CF trackers. In this paper, we make the first attempt to address this issue by introducing a family of 1D boundary CFs to localize the left, right, top, and bottom boundaries in videos. This allows us cope with the aspect ratio variation flexibly during tracking. Specifically, we present a novel tracking model to integrate 1D Boundary and 2D Center CFs (IBCCF) where boundary and center filters are enforced by a near-orthogonality regularization term. To optimize our IBCCF model, we develop an alternating direction method of multipliers. Experiments on several datasets show that IBCCF can effectively handle aspect ratio variation, and achieves state-of-the-art performance in terms of accuracy and robustness.

Connected through hyperedge HE-1 (induced by source paper "keyfilter aware real time uav object tracking"):
  with members:
    - "can a robot become a movie director learning artistic principles for aerial cinematography"
    - "learning aberrance repressed correlation filters for real time uav tracking"
    - "unsupervised deep tracking"
    - "boundary effect aware visual tracking for uav with online enhanced background learning and multi frame consensus verification"
    - "learning spatial temporal regularized correlation filters for visual tracking"
    - "kernel cross correlator"
    - "end to end representation learning for correlation filter based tracking"
    - "learning background aware correlation filters for visual tracking"

Connected through hyperedge HE-2 (induced by source paper "unsupervised deep tracking"):
  with members:
    - "real time mdnet"
    - "long term tracking in the wild a benchmark"
    - "a twofold siamese network for real time object tracking"
    - "end to end active object tracking and its real world deployment via reinforcement learning"
    - "deep attentive tracking via reciprocative learning"
    - "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"

Connected through hyperedge HE-3 (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"
    - "end to end representation learning for correlation filter based tracking"
    - "eco efficient convolution operators for tracking"

Connected through hyperedge HE-4 (induced by source paper "augmented memory for correlation filters in real time uav tracking"):
  with members:
    - "can a robot become a movie director learning artistic principles for aerial cinematography"
    - "learning aberrance repressed correlation filters for real time uav tracking"
    - "towards a robust aerial cinematography platform localizing and tracking moving targets in unstructured environments"
    - "boundary effect aware visual tracking for uav with online enhanced background learning and multi frame consensus verification"
    - "target aware deep tracking"
    - "the unmanned aerial vehicle benchmark object detection and tracking"
    - "learning spatial temporal regularized correlation filters for visual tracking"
    - "kernel cross correlator"

Connected through hyperedge HE-5 (induced by source paper "siamcar siamese fully convolutional classification and regression for visual tracking"):
  with members:
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "spm tracker series parallel matching for real time visual object tracking"
    - "fcos fully convolutional one stage object detection"
    - "distractor aware siamese networks for visual object tracking"
    - "a twofold siamese network for real time object tracking"
    - "deep attentive tracking via reciprocative learning"
    - "siamrpn evolution of siamese visual tracking with very deep networks"
    - "end to end representation learning for correlation filter based tracking"

Connected through hyperedge HE-6 (induced by source paper "learning spatial temporal regularized correlation filters for visual tracking"):
  with members:
    - "end to end representation learning for correlation filter based tracking"
    - "robust and real time deep tracking via multi scale domain adaptation"
    - "large margin object tracking with circulant feature maps"
    - "learning background aware correlation filters for visual tracking"
    - "eco efficient convolution operators for tracking"
    - "visual object tracking performance measures revisited"
    - "fully convolutional siamese networks for object tracking"
    - "learning support correlation filters for visual 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)