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 "a tracking system for baseball game reconstruction"):
  Abstract: The baseball game is often seen as many contests that are performed between individuals. The duel between the pitcher and the batter, for example, is considered the engine that drives the sport. The pitchers use a variety of strategies to gain competitive advantage against the batter, who does his best to figure out the ball trajectory and react in time for a hit. In this work, we propose a system that captures the movements of the pitcher, the batter, and the ball in a high level of detail, and discuss several ways how this information may be processed to compute interesting statistics. We demonstrate on a large database of videos that our methods achieve comparable results as previous systems, while operating solely on video material. In addition, state-of-the-art AI techniques are incorporated to augment the amount of information that is made available for players, coaches, teams, and fans.

Includes members:
  Member "event based vision a survey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "matrix lstm a differentiable recurrent surface for asynchronous event based data")
  Member "the world of fast moving objects":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "aggregation signature for small object tracking")
  Member "learning to detect human object interactions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "visual semantic graph attention networks for human object interaction detection")
  Member "towards accurate multi person pose estimation in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "higherhrnet scale aware representation learning for bottom up human pose estimation")
  Member "cascaded pyramid network for multi person pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "higherhrnet scale aware representation learning for bottom up human pose estimation")
  Member "vnect real time 3d human pose estimation with a single rgb camera":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation")
  Member "rmpe regional multi person pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hierarchical human parsing with typed part relation reasoning")
  Member "action recognition based on joint trajectory maps using convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a survey on 3d skeleton based action recognition using learning method")

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)