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: "dual path networks for multi person human pose estimation"
  Abstract: The task of multi-person human pose estimation in natural scenes is quite challenging. Existing methods include both top-down and bottom-up approaches. The main advantage of bottom-up methods is its excellent tradeoff between estimation accuracy and computational cost. We follow this path and aim to design smaller, faster, and more accurate neural networks for the regression of keypoints and limb association vectors. These two regression tasks are naturally dependent on each other. In this work, we propose a dual-path network specially designed for multi-person human pose estimation, and compare our performance with the openpose network in aspects of model size, forward speed, and estimation accuracy.

Connected through hyperedge HE-1 (induced by source paper "pose estimator and tracker using temporal flow maps for limbs"):
  with members:
    - "pose flow efficient online pose tracking"
    - "joint flow temporal flow fields for multi person tracking"
    - "simple baselines for human pose estimation and tracking"
    - "learning feature pyramids for human pose estimation"
    - "towards accurate multi person pose estimation in the wild"
    - "adversarial posenet a structure aware convolutional network for human pose estimation"
    - "posetrack a benchmark for human pose estimation and tracking"
    - "multi context attention for human pose estimation"

Connected through hyperedge HE-2 (induced by source paper "large area 3d human pose detection via stereo reconstruction in panoramic cameras"):
  with members:
    - "simple online and realtime tracking with a deep association metric"
    - "posetrack joint multi person pose estimation and tracking"
    - "pose for action action for pose"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "flowing convnets for human pose estimation in videos"
    - "very deep convolutional networks for large scale image recognition"
    - "deeppose human pose estimation via deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "multi scale structure aware network for human pose estimation"):
  with members:
    - "learning feature pyramids for human pose estimation"
    - "self adversarial training for human pose estimation"
    - "human pose regression by combining indirect part detection and contextual information"
    - "adversarial posenet a structure aware convolutional network for human pose estimation"
    - "multi context attention for human pose estimation"
    - "diversified visual attention networks for fine grained object classification"
    - "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)