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: "deep geometric prior for surface reconstruction"
  Abstract: The reconstruction of a discrete surface from a point cloud is a fundamental geometry processing problem that has been studied for decades, with many methods developed. We propose the use of a deep neural network as a geometric prior for surface reconstruction. Specifically, we overfit a neural network representing a local chart parameterization to part of an input point cloud using the Wasserstein distance as a measure of approximation. By jointly fitting many such networks to overlapping parts of the point cloud, while enforcing a consistency condition, we compute a manifold atlas. By sampling this atlas, we can produce a dense reconstruction of the surface approximating the input cloud. The entire procedure does not require any training data or explicit regularization, yet, we show that it is able to perform remarkably well: not introducing typical overfitting artifacts, and approximating sharp features closely at the same time. We experimentally show that this geometric prior produces good results for both man-made objects containing sharp features and smoother organic objects, as well as noisy inputs. We compare our method with a number of well-known reconstruction methods on a standard surface reconstruction benchmark.

Connected through hyperedge HE-1 (induced by source paper "implicit geometric regularization for learning shapes"):
  with members:
    - "controlling neural level sets"
    - "sdfdiff differentiable rendering of signed distance fields for 3d shape optimization"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "cvxnets learnable convex decomposition"
    - "learning elementary structures for 3d shape generation and matching"
    - "bsp net generating compact meshes via binary space partitioning"
    - "voronoinet general functional approximators with local support"
    - "deep level sets implicit surface representations for 3d shape inference"

Connected through hyperedge HE-2 (induced by source paper "meshlet priors for 3d mesh reconstruction"):
  with members:
    - "photometric mesh optimization for video aligned 3d object reconstruction"
    - "towards robust monocular depth estimation mixing datasets for zero shot cross dataset transfer"
    - "multi chart generative surface modeling"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "what do single view 3d reconstruction networks learn"
    - "learning category specific mesh reconstruction from image collections"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "deepmvs learning multi view stereopsis"

Connected through hyperedge HE-3 (induced by source paper "deep depth prior for multi view stereo"):
  with members:
    - "a bayesian perspective on the deep image prior"
    - "learned collaborative stereo refinement"
    - "double dip unsupervised image decomposition via coupled deep image priors"
    - "neural inverse rendering for general reflectance photometric stereo"
    - "deepmvs learning multi view stereopsis"
    - "continuous 3d label stereo matching using local expansion moves"
    - "mvsnet depth inference for unstructured multi view stereo"
    - "pyramid stereo matching network"

Connected through hyperedge HE-4 (induced by source paper "meshcnn a network with an edge"):
  with members:
    - "spherical cnns on unstructured grids"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "alignet partial shape agnostic alignment via unsupervised learning"
    - "dual primal graph convolutional networks"
    - "hierarchical graph representation learning with differentiable pooling"
    - "point convolutional neural networks by extension operators"
    - "multi directional geodesic neural networks via equivariant convolution"
    - "learning 3d shapes as multi layered height maps using 2d convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "deep sketch based modeling of man made shapes"):
  with members:
    - "deepspline data driven reconstruction of parametric curves and surfaces"
    - "learning shape templates with structured implicit functions"
    - "multi chart generative surface modeling"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "learning to infer and execute 3d shape programs"
    - "3dn 3d deformation network"
    - "deep parametric shape predictions using distance fields"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"

Connected through hyperedge HE-6 (induced by source paper "bae net branched autoencoder for shape co segmentation"):
  with members:
    - "cosegnet deep co segmentation of 3d shapes with group consistency loss"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "deep part induction from articulated object pairs"
    - "3d point capsule networks"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "weakly supervised semantic segmentation by iteratively mining common object features"
    - "adversarial complementary learning for weakly supervised object localization"

Connected through hyperedge HE-7 (induced by source paper "bsp net generating compact meshes via binary space partitioning"):
  with members:
    - "learning shape templates with structured implicit functions"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "cvxnets learnable convex decomposition"
    - "logan unpaired shape transform in latent overcomplete space"
    - "bae net branched autoencoder for shape co segmentation"
    - "sdm net deep generative network for structured deformable mesh"
    - "superquadrics revisited learning 3d shape parsing beyond cuboids"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"

Connected through hyperedge HE-8 (induced by source paper "controlling neural level sets"):
  with members:
    - "multi chart generative surface modeling"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "theoretically principled trade off between robustness and accuracy"
    - "advertorch v0 1 an adversarial robustness toolbox based on pytorch"
    - "scaling provable adversarial defenses"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "decoupling direction and norm for efficient gradient based l2 adversarial attacks and defenses"
    - "learning implicit fields for generative shape modeling"

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)