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: "boundary effect aware visual tracking for uav with online enhanced background learning and multi frame consensus verification"
  Abstract: Due to implicitly introduced periodic shifting of limited searching area, visual object tracking using correlation filters often has to confront undesired boundary effect. As boundary effect severely degrade the quality of object model, it has made it a challenging task for unmanned aerial vehicles (UAV) to perform robust and accurate object following. Traditional hand-crafted features are also not precise and robust enough to describe the object in the viewing point of UAV. In this work, a novel tracker with online enhanced background learning is specifically proposed to tackle boundary effects. Real background samples are densely extracted to learn as well as update correlation filters. Spatial penalization is introduced to offset the noise introduced by exceedingly more background information so that a more accurate appearance model can be established. Meanwhile, convolutional features are extracted to provide a more comprehensive representation of the object. In order to mitigate changes of objects' appearances, multi-frame technique is applied to learn an ideal response map and verify the generated one in each frame. Exhaustive experiments were conducted on 100 challenging UAV image sequences and the proposed tracker has achieved state-of-the-art performance.

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"
    - "learning spatial temporal regularized correlation filters for visual tracking"
    - "kernel cross correlator"
    - "integrating boundary and center correlation filters for visual tracking with aspect ratio variation"
    - "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 "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"
    - "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"
    - "integrating boundary and center correlation filters for visual tracking with aspect ratio variation"

Connected through hyperedge HE-3 (induced by source paper "learning aberrance repressed correlation filters for real time uav tracking"):
  with members:
    - "the unmanned aerial vehicle benchmark object detection and tracking"
    - "learning spatial temporal regularized correlation filters 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"
    - "siamese instance search 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)