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: "predicting people s 3d poses from short sequences"
  Abstract: We propose an efficient approach to exploiting motion information from consecutive frames of a video sequence to recover the 3D pose of people. Instead of computing candidate poses in individual frames and then linking them, as is often done, we regress directly from a spatio-temporal block of frames to a 3D pose in the central one. We will demonstrate that this approach allows us to effectively overcome ambiguities and to improve upon the state-of-the-art on challenging sequences.

Connected through hyperedge HE-1 (induced by source paper "rethinking pose in 3d multi stage refinement and recovery for markerless motion capture"):
  with members:
    - "adversarial inverse graphics networks learning 2d to 3d lifting and image to image translation from unpaired supervision"
    - "sparse representation for 3d shape estimation a convex relaxation approach"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "a generative model of people in clothing"
    - "harvesting multiple views for marker less 3d human pose annotations"
    - "self supervised learning of motion capture"
    - "compositional human pose regression"

Connected through hyperedge HE-2 (induced by source paper "a light dual task neural network for haze removal"):
  with members:
    - "gated fusion network for single image dehazing"
    - "fisheyerecnet a multi context collaborative deep network for fisheye image rectification"
    - "improving object detection from scratch via gated feature reuse"
    - "deep voice real time neural text to speech"
    - "an all in one network for dehazing and beyond"
    - "tensorlayer a versatile library for efficient deep learning development"
    - "dehazenet an end to end system for single image haze removal"
    - "what players do with the ball a physically constrained interaction modeling"

Connected through hyperedge HE-3 (induced by source paper "3d human pose estimation from depth maps using a deep combination of poses"):
  with members:
    - "real time human motion capture with multiple depth cameras"
    - "towards viewpoint invariant 3d human pose estimation"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "matconvnet convolutional neural networks for matlab"

Connected through hyperedge HE-4 (induced by source paper "lifting from the deep convolutional 3d pose estimation from a single image"):
  with members:
    - "sparse representation for 3d shape estimation a convex relaxation approach"
    - "convolutional pose machines"
    - "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"
    - "structured prediction of 3d human pose with deep neural networks"
    - "a simple fast and highly accurate algorithm to recover 3d shape from 2d landmarks on a single image"
    - "single image 3d interpreter network"

Connected through hyperedge HE-5 (induced by source paper "3d human pose estimation from a single image via distance matrix regression"):
  with members:
    - "sparse representation for 3d shape estimation a convex relaxation approach"
    - "convolutional pose machines"
    - "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"
    - "structured prediction of 3d human pose with deep neural networks"
    - "sparseness meets deepness 3d human pose estimation from monocular video"
    - "flownet learning optical flow with convolutional networks"
    - "deepcut joint subset partition and labeling for multi person pose estimation"

Connected through hyperedge HE-6 (induced by source paper "human pose estimation in space and time using 3d cnn"):
  with members:
    - "sparseness meets deepness 3d human pose estimation from monocular video"
    - "hands deep in deep learning for hand pose estimation"
    - "combining local appearance and holistic view dual source deep neural networks for human pose estimation"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "robust estimation of 3d human poses from a single image"
    - "efficient object localization using convolutional networks"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "deeppose human pose estimation via deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "sparseness meets deepness 3d human pose estimation from monocular video"):
  with members:
    - "sparse representation for 3d shape estimation a convex relaxation approach"
    - "maximum margin structured learning with deep networks for 3d human pose estimation"
    - "flowing convnets for human pose estimation in videos"
    - "robust estimation of 3d human poses from a single image"
    - "joint training of a convolutional network and a graphical model for human pose estimation"
    - "3d shape estimation from 2d landmarks a convex relaxation approach"
    - "caffe convolutional architecture for fast feature embedding"
    - "articulated pose estimation by a graphical model with image dependent pairwise relations"

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)