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: "framenet learning local canonical frames of 3d surfaces from a single rgb image"
  Abstract: In this work, we introduce the novel problem of identifying dense canonical 3D coordinate frames from a single RGB image. We observe that each pixel in an image corresponds to a surface in the underlying 3D geometry, where a canonical frame can be identified as represented by three orthogonal axes, one along its normal direction and two in its tangent plane. We propose an algorithm to predict these axes from RGB. Our first insight is that canonical frames computed automatically with recently introduced direction field synthesis methods can provide training data for the task. Our second insight is that networks designed for surface normal prediction provide better results when trained jointly to predict canonical frames, and even better when trained to also predict 2D projections of canonical frames. We conjecture this is because projections of canonical tangent directions often align with local gradients in images, and because those directions are tightly linked to 3D canonical frames through projective geometry and orthogonality constraints. In our experiments, we find that our method predicts 3D canonical frames that can be used in applications ranging from surface normal estimation, feature matching, and augmented reality.

Connected through hyperedge HE-1 (induced by source paper "the whole is greater than the sum of its nonrigid parts"):
  with members:
    - "amass archive of motion capture as surface shapes"
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "meshcnn a network with an edge"
    - "correspondence free region localization for partial shape similarity via hamiltonian spectrum alignment"
    - "meteornet deep learning on dynamic 3d point cloud sequences"
    - "deep hough voting for 3d object detection in point clouds"
    - "bodynet volumetric inference of 3d human body shapes"

Connected through hyperedge HE-2 (induced by source paper "adversarial texture optimization from rgb d scans"):
  with members:
    - "deferred neural rendering image synthesis using neural textures"
    - "neural point based graphics"
    - "deepvoxels learning persistent 3d feature embeddings"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "robust watertight manifold surface generation method for shapenet models"
    - "perceptual losses for real time style transfer and super resolution"
    - "image to image translation with conditional adversarial networks"
    - "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration"

Connected through hyperedge HE-3 (induced by source paper "uprightnet geometry aware camera orientation estimation from single images"):
  with members:
    - "learning the depths of moving people by watching frozen people"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "planenet piece wise planar reconstruction from a single rgb image"
    - "groundnet segmentation aware monocular ground plane estimation with geometric consistency"
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "deep depth completion of a single rgb d image"
    - "texturenet consistent local parametrizations for learning from high resolution signals on meshes"

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)