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: "a multi view rgb d approach for human pose estimation in operating rooms"
  Abstract: Many approaches have been proposed for human pose estimation in single and multi-view RGB images. However, some environments, such as the operating room, are still very challenging for state-of-the-art RGB methods. In this paper, we propose an approach for multi-view 3D human pose estimation from RGB-D images and demonstrate the benefits of using the additional depth channel for pose refinement beyond its use for the generation of improved features. The proposed method permits the joint detection and estimation of the poses without knowing a priori the number of persons present in the scene. We evaluate this approach on a novel multi-view RGB-D dataset acquired during live surgeries and annotated with ground truth 3D poses.

Connected through hyperedge HE-1 (induced by source paper "towards robust rgb d human mesh recovery"):
  with members:
    - "repnet weakly supervised training of an adversarial reprojection network for 3d human pose estimation"
    - "learning elementary structures for 3d shape generation and matching"
    - "neural scene decomposition for multi person motion capture"
    - "exploiting temporal context for 3d human pose estimation in the wild"
    - "weakly supervised discovery of geometry aware representation for 3d human pose estimation"
    - "shape aware human pose and shape reconstruction using multi view images"
    - "ordinal depth supervision for 3d human pose estimation"
    - "vision based pose estimation for augmented reality a comparison study"

Connected through hyperedge HE-2 (induced by source paper "real time multiple people hand localization in 4d point clouds"):
  with members:
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "densepose dense human pose estimation in the wild"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "end to end recovery of human shape and pose"
    - "3d semantic segmentation with submanifold sparse convolutional networks"
    - "o cnn octree based convolutional neural networks for 3d shape analysis"
    - "segcloud semantic segmentation of 3d point clouds"

Connected through hyperedge HE-3 (induced by source paper "face detection in the operating room comparison of state of the art methods and a self supervised approach"):
  with members:
    - "simple baselines for human pose estimation and tracking"
    - "faceoff anonymizing videos in the operating rooms"
    - "mvor a multi view rgb d operating room dataset for 2d and 3d human pose estimation"
    - "s 3 fd single shot scale invariant face detector"
    - "data distillation towards omni supervised learning"
    - "surgical data science for next generation interventions"
    - "articulated clinician detection using 3d pictorial structures on rgb d data"
    - "ssh single stage headless face detector"

Connected through hyperedge HE-4 (induced by source paper "a generalizable approach for multi view 3d human pose regression"):
  with members:
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "multi view pose estimation with mixtures of parts and adaptive viewpoint selection"
    - "harvesting multiple views for marker less 3d human pose annotations"
    - "deepskeleton skeleton map for 3d human pose regression"
    - "a simple yet effective baseline for 3d human pose estimation"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "coarse to fine volumetric prediction for single image 3d human pose"

Connected through hyperedge HE-5 (induced by source paper "real time convolutional networks for depth based human pose estimation"):
  with members:
    - "an analysis of deep neural network models for practical applications"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "learning from simulated and unsupervised images through adversarial training"
    - "human pose estimation from depth images via inference embedded multi task learning"
    - "deepercut a deeper stronger and faster multi person pose estimation model"
    - "human pose estimation via convolutional part heatmap regression"
    - "towards viewpoint invariant 3d human pose estimation"

Connected through hyperedge HE-6 (induced by source paper "mvor a multi view rgb d operating room dataset for 2d and 3d human pose estimation"):
  with members:
    - "a generalizable approach for multi view 3d human pose regression"
    - "deformable convolutional networks"
    - "rmpe regional multi person pose estimation"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "r fcn object detection via region based fully convolutional networks"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "microsoft coco common objects in context"

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)