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: "sparse inertial poser automatic 3d human pose estimation from sparse imus"
  Abstract: We address the problem of making human motion capture in the wild more practical by using a small set of inertial sensors attached to the body. Since the problem is heavily under-constrained, previous methods either use a large number of sensors, which is intrusive, or they require additional video input. We take a different approach and constrain the problem by: (i) making use of a realistic statistical body model that includes anthropometric constraints and (ii) using a joint optimization framework to fit the model to orientation and acceleration measurements over multiple frames. The resulting tracker Sparse Inertial Poser (SIP) enables 3D human pose estimation using only 6 sensors (attached to the wrists, lower legs, back and head) and works for arbitrary human motions. Experiments on the recently released TNT15 dataset show that, using the same number of sensors, SIP achieves higher accuracy than the dataset baseline without using any video data. We further demonstrate the effectiveness of SIP on newly recorded challenging motions in outdoor scenarios such as climbing or jumping over a wall.

Connected through hyperedge HE-1 (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-2 (induced by source paper "deepfuse an imu aware network for real time 3d human pose estimation from multi view image"):
  with members:
    - "self supervised learning of 3d human pose using multi view geometry"
    - "fast and robust multi person 3d pose estimation from multiple views"
    - "rethinking pose in 3d multi stage refinement and recovery for markerless motion capture"
    - "a generalizable approach for multi view 3d human pose regression"
    - "multi scale structure aware network for human pose estimation"
    - "deep autoencoder for combined human pose estimation and body model upscaling"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "3d human pose estimation with 2d marginal heatmaps"

Connected through hyperedge HE-3 (induced by source paper "imapper interaction guided scene mapping from monocular videos"):
  with members:
    - "lcr net multi person 2d and 3d pose detection in natural images"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "detecting and recognizing human object interactions"
    - "seethrough finding chairs in heavily occluded indoor scene images"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "matterport3d learning from rgb d data in indoor environments"
    - "vnect real time 3d human pose estimation with a single rgb camera"
    - "factoring shape pose and layout from the 2d image of a 3d scene"

Connected through hyperedge HE-4 (induced by source paper "learning to estimate 3d human pose and shape from a single color image"):
  with members:
    - "sparse representation for 3d shape estimation a convex relaxation approach"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "towards accurate markerless human shape and pose estimation over time"
    - "monoperfcap human performance capture from monocular video"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "compositional human pose regression"

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 "deep neural network based cooperative visual tracking through multiple micro aerial vehicles"):
  with members:
    - "ssd single shot multibox detector"
    - "deep learning in neural networks"
    - "taking a deeper look at pedestrians"

Connected through hyperedge HE-8 (induced by source paper "a kinematic chain space for monocular motion capture"):
  with members:
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "end to end recovery of human shape and pose"
    - "optical flow based 3d human motion estimation from monocular video"
    - "unite the people closing the loop between 3d and 2d human representations"
    - "a simple yet effective baseline for 3d human pose estimation"
    - "coarse to fine volumetric prediction for single image 3d human pose"
    - "keep it smpl automatic estimation of 3d human pose and shape from a single image"
    - "deepercut a deeper stronger and faster multi person pose estimation model"

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)