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: "3d shape estimation from 2d landmarks a convex relaxation approach"
  Abstract: We investigate the problem of estimating the 3D shape of an object, given a set of 2D landmarks in a single image. To alleviate the reconstruction ambiguity, a widely-used approach is to confine the unknown 3D shape within a shape space built upon existing shapes. While this approach has proven to be successful in various applications, a challenging issue remains, i.e., the joint estimation of shape parameters and camera-pose parameters requires to solve a nonconvex optimization problem. The existing methods often adopt an alternating minimization scheme to locally update the parameters, and consequently the solution is sensitive to initialization. In this paper, we propose a convex formulation to address this problem and develop an efficient algorithm to solve the proposed convex program. We demonstrate the exact recovery property of the proposed method, its merits compared to alternative methods, and the applicability in human pose and car shape estimation.

Connected through hyperedge HE-1 (induced by source paper "joint face alignment and 3d face reconstruction with application to face recognition"):
  with members:
    - "representation learning by rotating your faces"
    - "densereg fully convolutional dense shape regression in the wild"
    - "nonlinear 3d face morphable model"
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "pose invariant face alignment with a single cnn"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "robust face alignment using a mixture of invariant experts"
    - "fitting a 3d morphable model to edges a comparison between hard and soft correspondences"

Connected through hyperedge HE-2 (induced by source paper "generalizing monocular 3d human pose estimation in the wild"):
  with members:
    - "ordinal depth supervision for 3d human pose estimation"
    - "single view stereo matching"
    - "3d human pose estimation in the wild by adversarial learning"
    - "fbi pose towards bridging the gap between 2d images and 3d human poses using forward or backward information"
    - "exploiting temporal information for 3d human pose estimation"
    - "ai challenger a large scale dataset for going deeper in image understanding"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "lifting from the deep convolutional 3d pose estimation from a single image"

Connected through hyperedge HE-3 (induced by source paper "trajectory space factorization for deep video based 3d human pose estimation"):
  with members:
    - "semantic graph convolutional networks for 3d human pose regression"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "3d human pose estimation in the wild by adversarial learning"
    - "exploiting temporal information for 3d human pose estimation"
    - "learning pose grammar to encode human body configuration for 3d pose estimation"
    - "wav2letter an end to end convnet based speech recognition system"
    - "compositional human pose regression"
    - "a simple yet effective baseline for 3d human pose estimation"

Connected through hyperedge HE-4 (induced by source paper "absposelifter absolute 3d human pose lifting network from a single noisy 2d human pose"):
  with members:
    - "posefix model agnostic general human pose refinement network"
    - "simple baselines for human pose estimation and tracking"
    - "3d human pose estimation in the wild by adversarial learning"
    - "numerical coordinate regression with convolutional neural networks"
    - "synthetic occlusion augmentation with volumetric heatmaps for the 2018 eccv posetrack challenge on 3d human pose estimation"
    - "monocap monocular human motion capture using a cnn coupled with a geometric prior"
    - "benchmarking and error diagnosis in multi instance pose estimation"
    - "learning pose grammar to encode human body configuration for 3d pose estimation"

Connected through hyperedge HE-5 (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"
    - "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 simple yet effective baseline for 3d human pose estimation"
    - "learning from synthetic humans"

Connected through hyperedge HE-6 (induced by source paper "humanmeshnet polygonal mesh recovery of humans"):
  with members:
    - "3d hand shape and pose estimation from a single rgb image"
    - "convolutional mesh regression for single image human shape reconstruction"
    - "3d hand shape and pose from images in the wild"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "bodynet volumetric inference of 3d human body shapes"
    - "monocular total capture posing face body and hands in the wild"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-7 (induced by source paper "learning local descriptors by optimizing the keypoint correspondence criterion applications to face matching learning from unlabeled videos and 3d shape retrieval"):
  with members:
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "warpnet weakly supervised matching for single view reconstruction"
    - "pn net conjoined triple deep network for learning local image descriptors"
    - "unsupervised learning of visual representations using videos"
    - "tilde a temporally invariant learned detector"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-8 (induced by source paper "deep interpretable non rigid structure from motion"):
  with members:
    - "multilayer convolutional sparse modeling pursuit and dictionary learning"
    - "scalable dense non rigid structure from motion a grassmannian perspective"
    - "estimating depth from rgb and sparse sensing"
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "parse geometry from a line monocular depth estimation with partial laser observation"
    - "single image 3d interpreter network"
    - "shapenet an information rich 3d model repository"
    - "render for cnn viewpoint estimation in images using cnns trained with rendered 3d model views"

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)