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 representation for 3d shape estimation a convex relaxation approach"
  Abstract: We investigate the problem of estimating the 3D shape of an object defined by a set of 3D landmarks, given their 2D correspondences in a single image. A successful approach to alleviating the reconstruction ambiguity is the 3D deformable shape model and a sparse representation is often used to capture complex shape variability. But the model inference is still challenging due to the nonconvexity in the joint optimization of shape and viewpoint. In contrast to prior work that relies on an alternating scheme whose solution depends on initialization, we propose a convex approach to addressing this challenge and develop an efficient algorithm to solve the proposed convex program. We further propose a robust model to handle gross errors in the 2D correspondences. We demonstrate the exact recovery property of the proposed method, the advantage compared to several nonconvex baselines and the applicability to recover 3D human poses and car models from single images.

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"
    - "unsupervised learning of disentangled representations from video"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "deep representation learning for human motion prediction and classification"

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 "monocular 3d human pose estimation by generation and ordinal ranking"):
  with members:
    - "ordinal depth supervision for 3d human pose estimation"
    - "a variational u net for conditional appearance and shape generation"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "drpose3d depth ranking in 3d human pose estimation"
    - "it s all relative monocular 3d human pose estimation from weakly supervised data"
    - "cross modal deep variational hand pose estimation"
    - "exploiting temporal information for 3d human pose estimation"
    - "crossing nets combining gans and vaes with a shared latent space for hand pose estimation"

Connected through hyperedge HE-4 (induced by source paper "not all parts are created equal 3d pose estimation by modelling bi directional dependencies of body parts"):
  with members:
    - "ordinal depth supervision for 3d human pose estimation"
    - "simple baselines for human pose estimation and tracking"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "3d human pose estimation in the wild by adversarial learning"
    - "learning monocular 3d human pose estimation from multi view images"
    - "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"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"

Connected through hyperedge HE-5 (induced by source paper "repnet weakly supervised training of an adversarial reprojection network for 3d human pose estimation"):
  with members:
    - "learning to estimate 3d human pose and shape from a single color image"
    - "3d human pose estimation in the wild by adversarial learning"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "orinet a fully convolutional network for 3d human pose estimation"
    - "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"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "single shot multi person 3d pose estimation from monocular rgb"

Connected through hyperedge HE-6 (induced by source paper "3d human pose machines with self supervised learning"):
  with members:
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "end to end recovery of human shape and pose"
    - "multi task self supervised visual learning"
    - "compositional human pose regression"
    - "recurrent 3d pose sequence machines"
    - "convolutional pose machines"

Connected through hyperedge HE-7 (induced by source paper "joint representation of multiple geometric priors via a shape decomposition model for single monocular 3d pose estimation"):
  with members:
    - "3d human pose machines with self supervised learning"
    - "ordinal depth supervision for 3d human pose estimation"
    - "monocap monocular human motion capture using a cnn coupled with a geometric prior"
    - "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 simple yet effective baseline for 3d human pose estimation"
    - "learning from synthetic humans"
    - "recurrent 3d pose sequence machines"

Connected through hyperedge HE-8 (induced by source paper "tree search network for sparse regression"):
  with members:
    - "theoretical linear convergence of unfolded ista and its practical weights and thresholds"
    - "amp inspired deep networks for sparse linear inverse problems"
    - "deep sparse coding using optimized linear expansion of thresholds"
    - "deep generative adversarial networks for compressed sensing automates mri"
    - "adaptive feature selection computationally efficient online sparse linear regression under rip"
    - "admm net a deep learning approach for compressive sensing mri"
    - "distributed compressive sensing a deep learning approach"
    - "greedy subspace pursuit for joint sparse recovery"

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)