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: "unsupervised domain adaptation for 3d keypoint estimation via view consistency"
  Abstract: In this paper, we introduce a novel unsupervised domain adaptation technique for the task of 3D keypoint prediction from a single depth scan or image. Our key idea is to utilize the fact that predictions from different views of the same or similar objects should be consistent with each other. Such view consistency can provide effective regularization for keypoint prediction on unlabeled instances. In addition, we introduce a geometric alignment term to regularize predictions in the target domain. The resulting loss function can be effectively optimized via alternating minimization. We demonstrate the effectiveness of our approach on real datasets and present experimental results showing that our approach is superior to state-of-the-art general-purpose domain adaptation techniques.

Connected through hyperedge HE-1 (induced by source paper "cross domain adaptation for animal pose estimation"):
  with members:
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"
    - "cross domain weakly supervised object detection through progressive domain adaptation"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "3d human pose estimation in the wild by adversarial learning"
    - "partial adversarial domain adaptation"
    - "instagan instance aware image to image translation"
    - "deep adversarial attention alignment for unsupervised domain adaptation the benefit of target expectation maximization"
    - "cross modal deep variational hand pose estimation"

Connected through hyperedge HE-2 (induced by source paper "pointdan a multi scale 3d domain adaption network for point cloud representation"):
  with members:
    - "semantic transferable weakly supervised endoscopic lesions segmentation"
    - "domain adaptation for vehicle detection from bird s eye view lidar point cloud data"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "image super resolution using very deep residual channel attention networks"
    - "pvrnet point view relation neural network for 3d shape recognition"
    - "meshnet mesh neural network for 3d shape representation"
    - "pvnet a joint convolutional network of point cloud and multi view for 3d shape recognition"
    - "voxelnet end to end learning for point cloud based 3d object detection"

Connected through hyperedge HE-3 (induced by source paper "unsupervised geometry aware representation for 3d human pose estimation"):
  with members:
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "learning monocular 3d human pose estimation from multi view images"
    - "unsupervised learning of object frames by dense equivariant image labelling"
    - "adversarial inverse graphics networks learning 2d to 3d lifting and image to image translation from unpaired supervision"
    - "transformation grounded image generation network for novel 3d view synthesis"
    - "learning a multi view stereo machine"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "a generative model of people in clothing"

Connected through hyperedge HE-4 (induced by source paper "starmap for category agnostic keypoint and viewpoint estimation"):
  with members:
    - "learning category specific mesh reconstruction from image collections"
    - "benchmarking and error diagnosis in multi instance pose estimation"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "extreme clicking for efficient object annotation"
    - "learning feature pyramids for human pose estimation"
    - "convolutional networks for object category and 3d pose estimation from 2d images"
    - "click here human localized keypoints as guidance for viewpoint estimation"
    - "multi view supervision for single view reconstruction via differentiable ray consistency"

Connected through hyperedge HE-5 (induced by source paper "learning local rgb to cad correspondences for object pose estimation"):
  with members:
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "bop benchmark for 6d object pose estimation"
    - "domain transfer for 3d pose estimation from color images without manual annotations"
    - "3d interpreter networks for viewer centered wireframe modeling"
    - "end to end learning of keypoint detector and descriptor for pose invariant 3d matching"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "3d pose regression using convolutional neural networks"
    - "seethrough finding chairs in heavily occluded indoor scene images"

Connected through hyperedge HE-6 (induced by source paper "matching rgb images to cad models for object pose estimation"):
  with members:
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "bop benchmark for 6d object pose estimation"
    - "domain transfer for 3d pose estimation from color images without manual annotations"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "3d pose regression using convolutional neural networks"
    - "zero shot deep domain adaptation"
    - "real time seamless single shot 6d object pose prediction"
    - "self supervised intrinsic image decomposition"

Connected through hyperedge HE-7 (induced by source paper "scan2cad learning cad model alignment in rgb d scans"):
  with members:
    - "fusion volumetric object level slam"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "holistic 3d scene parsing and reconstruction from a single rgb image"
    - "matterport3d learning from rgb d data in indoor environments"
    - "scancomplete large scale scene completion and semantic segmentation for 3d scans"
    - "semantic scene completion from a single depth image"
    - "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration"
    - "shapenet an information rich 3d model repository"

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)