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: "kernel cross correlator"
  Abstract: Cross-correlator plays a significant role in many visual perception tasks, such as object detection and tracking. Beyond the linear cross-correlator, this paper proposes a kernel cross-correlator (KCC) that breaks traditional limitations. First, by introducing the kernel trick, the KCC extends the linear cross-correlation to non-linear space, which is more robust to signal noises and distortions. Second, the connection to the existing works shows that KCC provides a unified solution for correlation filters. Third, KCC is applicable to any kernel function and is not limited to circulant structure on training data, thus it is able to predict affine transformations with customized properties. Last, by leveraging the fast Fourier transform (FFT), KCC eliminates direct calculation of kernel vectors, thus achieves better performance yet still with a reasonable computational cost. Comprehensive experiments on visual tracking and human activity recognition using wearable devices demonstrate its robustness, flexibility, and efficiency. The source codes of both experiments are released at this https URL

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"
    - "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 "graph optimization approach to range based localization"):
  with members:
    - "kervolutional neural networks"
    - "ultra wideband aided fast localization and mapping system"
    - "non iterative slam"
    - "machine learning in wireless sensor networks algorithms strategies and applications"

Connected through hyperedge HE-3 (induced by source paper "kervolutional neural networks"):
  with members:
    - "decoupled networks"
    - "correlation flow robust optical flow using kernel cross correlators"
    - "non linear convolution filters for cnn based learning"
    - "ultra wideband aided fast localization and mapping system"
    - "non iterative slam"
    - "splinecnn fast geometric deep learning with continuous b spline kernels"
    - "dynamic routing between capsules"
    - "deformable convolutional networks"

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"
    - "integrating boundary and center correlation filters for visual tracking with aspect ratio variation"

Connected through hyperedge HE-5 (induced by source paper "robust target relative localization with ultra wideband ranging and communication"):
  with members:
    - "correlation flow robust optical flow using kernel cross correlators"
    - "ultra wideband aided fast localization and mapping system"
    - "3 d reciprocal collision avoidance on physical quadrotor helicopters with on board sensing for relative positioning"

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)