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: "learning pose specific representations by predicting different views"
  Abstract: The labeled data required to learn pose estimation for articulated objects is difficult to provide in the desired quantity, realism, density, and accuracy. To address this issue, we develop a method to learn representations, which are very specific for articulated poses, without the need for labeled training data. We exploit the observation that the object pose of a known object is predictive for the appearance in any known view. That is, given only the pose and shape parameters of a hand, the hand's appearance from any viewpoint can be approximated. To exploit this observation, we train a model that -- given input from one view -- estimates a latent representation, which is trained to be predictive for the appearance of the object when captured from another viewpoint. Thus, the only necessary supervision is the second view. The training process of this model reveals an implicit pose representation in the latent space. Importantly, at test time the pose representation can be inferred using only a single view. In qualitative and quantitative experiments we show that the learned representations capture detailed pose information. Moreover, when training the proposed method jointly with labeled and unlabeled data, it consistently surpasses the performance of its fully supervised counterpart, while reducing the amount of needed labeled samples by at least one order of magnitude.

Connected through hyperedge HE-1 (induced by source paper "dual grid net hand mesh vertex regression from single depth maps"):
  with members:
    - "3d hand shape and pose estimation from a single rgb image"
    - "pose guided structured region ensemble network for cascaded hand pose estimation"
    - "end to end hand mesh recovery from a monocular rgb image"
    - "3d hand shape and pose from images in the wild"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "bodynet volumetric inference of 3d human body shapes"
    - "densereg fully convolutional dense shape regression in the wild"

Connected through hyperedge HE-2 (induced by source paper "weakly supervised discovery of geometry aware representation for 3d human pose estimation"):
  with members:
    - "feature boosting network for 3d pose estimation"
    - "ordinal depth supervision for 3d human pose estimation"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "synthesizing images of humans in unseen poses"
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "deepmvs learning multi view stereopsis"
    - "deep autoencoder for combined human pose estimation and body model upscaling"

Connected through hyperedge HE-3 (induced by source paper "3d hand shape and pose from images in the wild"):
  with members:
    - "cross modal deep variational hand pose estimation"
    - "learning to estimate 3d hand pose from single rgb images"
    - "back to rgb 3d tracking of hands and hand object interactions based on short baseline stereo"
    - "using a single rgb frame for real time 3d hand pose estimation in the wild"
    - "end to end recovery of human shape and pose"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "dense 3d regression for hand pose estimation"

Connected through hyperedge HE-4 (induced by source paper "simultaneous multi view instance detection with learned geometric soft constraints"):
  with members:
    - "rpnet an end to end network for relative camera pose estimation"
    - "from google maps to a fine grained catalog of street trees"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "accurate deep direct geo localization from ground imagery and phone grade gps"
    - "toward seamless multiview scene analysis from satellite to street level"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "automatic discovery and geotagging of objects from street view imagery"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"

Connected through hyperedge HE-5 (induced by source paper "murauer mapping unlabeled real data for label austerity"):
  with members:
    - "pose guided structured region ensemble network for cascaded hand pose estimation"
    - "3d hand pose estimation using simulation and partial supervision with a shared latent space"
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "cross modal deep variational hand pose estimation"
    - "crossing nets combining gans and vaes with a shared latent space for hand pose estimation"
    - "learning to estimate 3d hand pose from single rgb images"
    - "using a single rgb frame for real time 3d hand pose estimation in the wild"
    - "v2v posenet voxel to voxel prediction network for accurate 3d hand and human pose estimation from a single depth map"

Connected through hyperedge HE-6 (induced by source paper "shapecodes self supervised feature learning by lifting views to viewgrids"):
  with members:
    - "deep view morphing"
    - "transitive invariance for self supervised visual representation learning"
    - "im2pano3d extrapolating 360 structure and semantics beyond the field of view"
    - "colorization as a proxy task for visual understanding"
    - "learning to look around intelligently exploring unseen environments for unknown tasks"
    - "look ahead before you leap end to end active recognition by forecasting the effect of motion"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "learning a predictable and generative vector representation for objects"

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)