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: "light cascaded convolutional neural networks for accurate player detection"
  Abstract: Vision based player detection is important in sports applications. Accuracy, efficiency, and low memory consumption are desirable for real-time tasks such as intelligent broadcasting and automatic event classification. In this paper, we present a cascaded convolutional neural network (CNN) that satisfies all three of these requirements. Our method first trains a binary (player/non-player) classification network from labeled image patches. Then, our method efficiently applies the network to a whole image in testing. We conducted experiments on basketball and soccer games. Experimental results demonstrate that our method can accurately detect players under challenging conditions such as varying illumination, highly dynamic camera movements and motion blur. Comparing with conventional CNNs, our approach achieves state-of-the-art accuracy on both games with 1000x fewer parameters (i.e., it is light}.

Connected through hyperedge HE-1 (induced by source paper "pan tilt zoom slam for sports videos"):
  with members:
    - "panoptic segmentation"
    - "scenecode monocular dense semantic reconstruction using learned encoded scene representations"
    - "let s take this online adapting scene coordinate regression network predictions for online rgb d camera relocalisation"
    - "sports camera calibration via synthetic data"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "a two point method for ptz camera calibration in sports"
    - "soccer on your tabletop"
    - "backtracking regression forests for accurate camera relocalization"

Connected through hyperedge HE-2 (induced by source paper "footandball integrated player and ball detector"):
  with members:
    - "deepball deep neural network ball detector"
    - "ssd single shot multibox detector"
    - "feature pyramid networks for object detection"
    - "you only look once unified real time object detection"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "a two point method for ptz camera calibration in sports"):
  with members:
    - "on the fly adaptation of regression forests for online camera relocalisation"
    - "backtracking regression forests for accurate camera relocalization"
    - "automated top view registration of broadcast football videos"
    - "latch learned arrangements of three patch codes"
    - "continuous localization and mapping of a pan tilt zoom camera for wide area 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)