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: "continuous video to simple signals for swimming stroke detection with convolutional neural networks"
  Abstract: In many sports, it is useful to analyse video of an athlete in competition for training purposes. In swimming, stroke rate is a common metric used by coaches; requiring a laborious labelling of each individual stroke. We show that using a Convolutional Neural Network (CNN) we can automatically detect discrete events in continuous video (in this case, swimming strokes). We create a CNN that learns a mapping from a window of frames to a point on a smooth 1D target signal, with peaks denoting the location of a stroke, evaluated as a sliding window. To our knowledge this process of training and utilizing a CNN has not been investigated before; either in sports or fundamental computer vision research. Most research has been focused on action recognition and using it to classify many clips in continuous video for action localisation. #R##N#In this paper we demonstrate our process works well on the task of detecting swimming strokes in the wild. However, without modifying the model architecture or training method, the process is also shown to work equally well on detecting tennis strokes, implying that this is a general process. #R##N#The outputs of our system are surprisingly smooth signals that predict an arbitrary event at least as accurately as humans (manually evaluated from a sample of negative results). A number of different architectures are evaluated, pertaining to slightly different problem formulations and signal targets.

Connected through hyperedge HE-1 (induced by source paper "towards automated swimming analytics using deep neural networks"):
  with members:
    - "yolov3 an incremental improvement"
    - "mv yolo motion vector aided tracking by semantic object detection"
    - "simple online and realtime tracking"

Connected through hyperedge HE-2 (induced by source paper "activity conditioned continuous human pose estimation for performance analysis of athletes using the example of swimming"):
  with members:
    - "thin slicing network a deep structured model for pose estimation in videos"
    - "extraction and classification of diving clips from continuous video footage"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "recurrent human pose estimation"
    - "chained predictions using convolutional neural networks"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "key pose prediction in cyclic human motion"

Connected through hyperedge HE-3 (induced by source paper "hand tremor frequency estimation in videos"):
  with members:
    - "real world repetition estimation by div grad and curl"
    - "parkinson s disease assessment from a wrist worn wearable sensor in free living conditions deep ensemble learning and visualization"
    - "video acceleration magnification"
    - "quasi periodicity quantification in video data using topology"
    - "convolutional pose machines"
    - "human pose estimation via convolutional part heatmap regression"
    - "making a case for learning motion representations with phase"
    - "deepcut joint subset partition and labeling for multi person pose estimation"

Connected through hyperedge HE-4 (induced by source paper "mining automatically estimated poses from video recordings of top athletes"):
  with members:
    - "activity conditioned continuous human pose estimation for performance analysis of athletes using the example of swimming"
    - "learning feature pyramids for human pose estimation"
    - "multi context attention for human pose estimation"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"

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)