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: "movement science needs different pose tracking algorithms"
  Abstract: Over the last decade, computer science has made progress towards extracting body pose from single camera photographs or videos. This promises to enable movement science to detect disease, quantify movement performance, and take the science out of the lab into the real world. However, current pose tracking algorithms fall short of the needs of movement science; the types of movement data that matter are poorly estimated. For instance, the metrics currently used for evaluating pose tracking algorithms use noisy hand-labeled ground truth data and do not prioritize precision of relevant variables like three-dimensional position, velocity, acceleration, and forces which are crucial for movement science. Here, we introduce the scientific disciplines that use movement data, the types of data they need, and discuss the changes needed to make pose tracking truly transformative for movement science.

Connected through hyperedge HE-1 (induced by source paper "from kinematics to dynamics estimating center of pressure and base of support from video frames of human motion"):
  with members:
    - "lcr net multi person 2d and 3d pose detection in natural images"
    - "densepose dense human pose estimation in the wild"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "compositional human pose regression"
    - "improved training of wasserstein gans"
    - "a simple yet effective baseline for 3d human pose estimation"
    - "stacked hourglass networks for human pose estimation"
    - "coarse to fine volumetric prediction for single image 3d human pose"

Connected through hyperedge HE-2 (induced by source paper "deep learning tools for the measurement of animal behavior in neuroscience"):
  with members:
    - "three d safari learning to estimate zebra pose shape and texture from images in the wild"
    - "pretraining boosts out of domain robustness for pose estimation"
    - "expressive body capture 3d hands face and body from a single image"
    - "pifpaf composite fields for human pose estimation"
    - "benchmarking robustness in object detection autonomous driving when winter is coming"
    - "deep high resolution representation learning for human pose estimation"
    - "detection and tracking of multiple mice using part proposal networks"
    - "human pose estimation for real world crowded scenarios"

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)