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: "the world of fast moving objects"
  Abstract: The notion of a Fast Moving Object (FMO), i.e. an object that moves over a distance exceeding its size within the exposure time, is introduced. FMOs may, and typically do, rotate with high angular speed. FMOs are very common in sports videos, but are not rare elsewhere. In a single frame, such objects are often barely visible and appear as semitransparent streaks. A method for the detection and tracking of FMOs is proposed. The method consists of three distinct algorithms, which form an efficient localization pipeline that operates successfully in a broad range of conditions. We show that it is possible to recover the appearance of the object and its axis of rotation, despite its blurred appearance. The proposed method is evaluated on a new annotated dataset. The results show that existing trackers are inadequate for the problem of FMO localization and a new approach is required. Two applications of localization, temporal superresolution and highlighting, are presented.

Connected through hyperedge HE-1 (induced by source paper "a tracking system for baseball game reconstruction"):
  with members:
    - "event based vision a survey"
    - "learning to detect human object interactions"
    - "towards accurate multi person pose estimation in the wild"
    - "cascaded pyramid network for multi person pose estimation"
    - "vnect real time 3d human pose estimation with a single rgb camera"
    - "rmpe regional multi person pose estimation"
    - "action recognition based on joint trajectory maps using convolutional neural networks"
    - "recurrent human pose estimation"

Connected through hyperedge HE-2 (induced by source paper "aggregation signature for small object tracking"):
  with members:
    - "circulant binary convolutional networks enhancing the performance of 1 bit dcnns with circulant back propagation"
    - "state aware anti drift object tracking"
    - "latent constrained correlation filter"
    - "distractor aware siamese networks for visual object tracking"
    - "robust estimation of similarity transformation for visual object tracking"
    - "eco efficient convolution operators for tracking"
    - "discriminative scale space tracking"
    - "learning spatially regularized correlation filters for visual tracking"

Connected through hyperedge HE-3 (induced by source paper "intra frame object tracking by deblatting"):
  with members:
    - "learning to extract a video sequence from a single motion blurred image"
    - "high speed tracking with multi kernel correlation filters"
    - "tracking for half an hour"
    - "learning blind motion deblurring"
    - "long term visual object tracking benchmark"
    - "a novel performance evaluation methodology for single target trackers"
    - "performance measures and a data set for multi target multi camera tracking"
    - "learning spatially regularized correlation filters for visual tracking"

Connected through hyperedge HE-4 (induced by source paper "sub frame appearance and 6d pose estimation of fast moving objects"):
  with members:
    - "non causal tracking by deblatting"
    - "learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking"
    - "intra frame object tracking by deblatting"
    - "learning to extract a video sequence from a single motion blurred image"
    - "end to end representation learning for correlation filter based tracking"
    - "learning blind motion deblurring"

Connected through hyperedge HE-5 (induced by source paper "non causal tracking by deblatting"):
  with members:
    - "intra frame object tracking by deblatting"
    - "features for multi target multi camera tracking and re identification"
    - "high speed tracking with multi kernel correlation filters"
    - "tracking for half an hour"
    - "long term visual object tracking benchmark"

Connected through hyperedge HE-6 (induced by source paper "high speed tracking with multi kernel correlation filters"):
  with members:
    - "end to end representation learning for correlation filter based tracking"
    - "need for speed a benchmark for higher frame rate object tracking"
    - "eco efficient convolution operators for tracking"
    - "discriminative scale space tracking"
    - "adaptive decontamination of the training set a unified formulation for discriminative visual tracking"
    - "learning spatially regularized correlation filters for visual tracking"
    - "online object tracking with proposal selection"
    - "staple complementary learners for real time 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)