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: "integral human pose regression"
  Abstract: State-of-the-art human pose estimation methods are dominated by complex multi-stage architectures using a heat map representation. The representation has a few issues in nature, such as no end-to-end training, quantization error and sensitivity to multiple modes. While pose estimation is essentially a regression problem, existing regression approaches are not as effective. This work proposes an integral regression approach that unifies the heat map representation and joint regression, thus possessing the merits of both paradigms. It is differentiable and allows end-to-end training. It is simple, fast, and compatible with any heat map based methods. Comprehensive experiments verify its efficacy. Building upon simple baseline networks, its performance on benchmarks is strong and competitive to state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "patch based 3d human pose refinement"):
  with members:
    - "pose guided structured region ensemble network for cascaded hand pose estimation"
    - "monocular 3d human pose estimation by generation and ordinal ranking"
    - "self supervised learning of 3d human pose using multi view geometry"
    - "ordinal depth supervision for 3d human pose estimation"
    - "rethinking pose in 3d multi stage refinement and recovery for markerless motion capture"
    - "task oriented hand motion retargeting for dexterous manipulation imitation"
    - "posefix model agnostic general human pose refinement network"
    - "bodynet volumetric inference of 3d human body shapes"

Connected through hyperedge HE-2 (induced by source paper "how robust is 3d human pose estimation to occlusion"):
  with members:
    - "an occluded stacked hourglass approach to facial landmark localization and occlusion estimation"
    - "multi scale structure aware network for human pose estimation"
    - "modeling visual context is key to augmenting object detection datasets"
    - "3d human pose estimation with 2d marginal heatmaps"
    - "numerical coordinate regression with convolutional neural networks"
    - "random erasing data augmentation"
    - "cut paste and learn surprisingly easy synthesis for instance detection"
    - "compositional human pose regression"

Connected through hyperedge HE-3 (induced by source paper "joint voxel and coordinate regression for accurate 3d facial landmark localization"):
  with members:
    - "faster than real time facial alignment a 3d spatial transformer network approach in unconstrained poses"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "multi context attention for human pose estimation"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "coarse to fine volumetric prediction for single image 3d human pose"
    - "two stage convolutional part heatmap regression for the 1st 3d face alignment in the wild 3dfaw challenge"
    - "human pose estimation via convolutional part heatmap regression"

Connected through hyperedge HE-4 (induced by source paper "an integral pose regression system for the eccv2018 posetrack challenge"):
  with members:
    - "towards accurate multi person pose estimation in the wild"
    - "compositional human pose regression"
    - "coarse to fine volumetric prediction for single image 3d human pose"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)