human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "spin a high speed high resolution vision dataset for tracking and action recognition in ping pong"):
  Abstract: We introduce a new high resolution, high frame rate stereo video dataset, which we call SPIN, for tracking and action recognition in the game of ping pong. The corpus consists of ping pong play with three main annotation streams that can be used to learn tracking and action recognition models -- tracking of the ping pong ball and poses of humans in the videos and the spin of the ball being hit by humans. The training corpus consists of 53 hours of data with labels derived from previous models in a semi-supervised method. The testing corpus contains 1 hour of data with the same information, except that crowd compute was used to obtain human annotations of the ball position, from which ball spin has been derived. Along with the dataset we introduce several baseline models that were trained on this data. The models were specifically chosen to be able to perform inference at the same rate as the images are generated -- specifically 150 fps. We explore the advantages of multi-task training on this data, and also show interesting properties of ping pong ball trajectories that are derived from our observational data, rather than from prior physics models. To our knowledge this is the first large scale dataset of ping pong; we offer it to the community as a rich dataset that can be used for a large variety of machine learning and vision tasks such as tracking, pose estimation, semi-supervised and unsupervised learning and generative modeling.

Includes members:
  Member "stochastic prediction of multi agent interactions from partial observations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "relational state space model for stochastic multi object systems")
  Member "dynamonet dynamic action and motion network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning spatiotemporal features via video and text pair discrimination")
  Member "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "higherhrnet scale aware representation learning for bottom up human pose estimation")
  Member "tracking noisy targets a review of recent object tracking approaches":
    also connected through other hyperedges:
      (none)
  Member "a proposal based solution to spatio temporal action detection in untrimmed videos":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "car pose in context accurate pose estimation with ground plane constraints")
  Member "video action transformer network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "pic permutation invariant convolution for recognizing long range activities")
  Member "representation flow for action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lightweight network architecture for real time action recognition")
  Member "a 2 nets double attention networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "semantic image networks for human action recognition")

Question: Please tell me which class the source paper 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)