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: "egocap egocentric marker less motion capture with two fisheye cameras"
  Abstract: Marker-based and marker-less optical skeletal motion-capture methods use an outside-in arrangement of cameras placed around a scene, with viewpoints converging on the center. They often create discomfort by possibly needed marker suits, and their recording volume is severely restricted and often constrained to indoor scenes with controlled backgrounds. Alternative suit-based systems use several inertial measurement units or an exoskeleton to capture motion. This makes capturing independent of a confined volume, but requires substantial, often constraining, and hard to set up body instrumentation. We therefore propose a new method for real-time, marker-less and egocentric motion capture which estimates the full-body skeleton pose from a lightweight stereo pair of fisheye cameras that are attached to a helmet or virtual reality headset. It combines the strength of a new generative pose estimation framework for fisheye views with a ConvNet-based body-part detector trained on a large new dataset. Our inside-in method captures full-body motion in general indoor and outdoor scenes, and also crowded scenes with many people in close vicinity. The captured user can freely move around, which enables reconstruction of larger-scale activities and is particularly useful in virtual reality to freely roam and interact, while seeing the fully motion-captured virtual body.

Connected through hyperedge HE-1 (induced by source paper "ego pose estimation and forecasting as real time pd control"):
  with members:
    - "xr egopose egocentric 3d human pose from an hmd camera"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "learning 3d human dynamics from video"
    - "sfv reinforcement learning of physical skills from videos"
    - "flow grounded spatial temporal video prediction from still images"
    - "sparse representation for 3d shape estimation a convex relaxation approach"
    - "unsupervised learning of disentangled representations from video"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"

Connected through hyperedge HE-2 (induced by source paper "xr egopose egocentric 3d human pose from an hmd camera"):
  with members:
    - "lcr net multi person 2d and 3d pose detection in natural images"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "can 3d pose be learned from 2d projections alone"
    - "exploiting temporal information for 3d human pose estimation"
    - "adversarial inverse graphics networks learning 2d to 3d lifting and image to image translation from unpaired supervision"
    - "monocap monocular human motion capture using a cnn coupled with a geometric prior"
    - "learning pose grammar to encode human body configuration for 3d pose estimation"

Connected through hyperedge HE-3 (induced by source paper "activemocap optimized drone flight for active human motion capture"):
  with members:
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "yolov3 an incremental improvement"
    - "learn to score efficient 3d scene exploration by predicting view utility"
    - "optimizing for aesthetically pleasing quadrotor camera motion"
    - "human motion capture using a drone"
    - "deep directional statistics pose estimation with uncertainty quantification"
    - "plan3d viewpoint and trajectory optimization for aerial multi view stereo reconstruction"
    - "lifting from the deep convolutional 3d pose estimation from a single image"

Connected through hyperedge HE-4 (induced by source paper "unsupervised geometry aware representation for 3d human pose estimation"):
  with members:
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "learning monocular 3d human pose estimation from multi view images"
    - "unsupervised learning of object frames by dense equivariant image labelling"
    - "adversarial inverse graphics networks learning 2d to 3d lifting and image to image translation from unpaired supervision"
    - "transformation grounded image generation network for novel 3d view synthesis"
    - "unsupervised domain adaptation for 3d keypoint estimation via view consistency"
    - "learning a multi view stereo machine"
    - "lifting from the deep convolutional 3d pose estimation from a single image"

Connected through hyperedge HE-5 (induced by source paper "mo2cap2 real time mobile 3d motion capture with a cap mounted fisheye camera"):
  with members:
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "harvesting multiple views for marker less 3d human pose annotations"
    - "learning from synthetic humans"
    - "vnect real time 3d human pose estimation with a single rgb camera"
    - "hand keypoint detection in single images using multiview bootstrapping"
    - "generating multiple diverse hypotheses for human 3d pose consistent with 2d joint detections"
    - "mask r cnn"

Connected through hyperedge HE-6 (induced by source paper "deep inertial poser learning to reconstruct human pose from sparse inertial measurements in real time"):
  with members:
    - "deep learning for sensor based activity recognition a survey"
    - "video based reconstruction of 3d people models"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "doublefusion real time capture of human performances with inner body shapes from a single depth sensor"
    - "learning human motion models for long term predictions"
    - "single shot multi person 3d pose estimation from monocular rgb"
    - "end to end recovery of human shape and pose"
    - "towards accurate markerless human shape and pose estimation over time"

Connected through hyperedge HE-7 (induced by source paper "learning monocular 3d human pose estimation from multi view images"):
  with members:
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "harvesting multiple views for marker less 3d human pose annotations"
    - "compositional human pose regression"
    - "a simple yet effective baseline for 3d human pose estimation"
    - "learning from synthetic humans"
    - "multi view supervision for single view reconstruction via differentiable ray consistency"
    - "vnect real time 3d human pose estimation with a single rgb camera"
    - "hand keypoint detection in single images using multiview bootstrapping"

Connected through hyperedge HE-8 (induced by source paper "vnect real time 3d human pose estimation with a single rgb camera"):
  with members:
    - "sparse representation for 3d shape estimation a convex relaxation approach"
    - "a versatile scene model with differentiable visibility applied to generative pose estimation"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "coarse to fine volumetric prediction for single image 3d human pose"
    - "learning to fuse 2d and 3d image cues for monocular body pose estimation"
    - "mocap guided data augmentation for 3d pose estimation in the wild"
    - "keep it smpl automatic estimation of 3d human pose and shape from a single image"

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)