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: "geodesics in heat a new approach to computing distance based on heat flow"
  Abstract: We introduce the heat method for computing the geodesic distance to a specified subset (e.g., point or curve) of a given domain. The heat method is robust, efficient, and simple to implement since it is based on solving a pair of standard linear elliptic problems. The resulting systems can be prefactored once and subsequently solved in near-linear time. In practice, distance is updated an order of magnitude faster than with state-of-the-art methods, while maintaining a comparable level of accuracy. The method requires only standard differential operators and can hence be applied on a wide variety of domains (grids, triangle meshes, point clouds, etc.). We provide numerical evidence that the method converges to the exact distance in the limit of refinement; we also explore smoothed approximations of distance suitable for applications where greater regularity is required.

Connected through hyperedge HE-1 (induced by source paper "nasa neural articulated shape approximation"):
  with members:
    - "implicit functions in feature space for 3d shape reconstruction and completion"
    - "amass archive of motion capture as surface shapes"
    - "deferred neural rendering image synthesis using neural textures"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "cvxnets learnable convex decomposition"
    - "unsupervised part based disentangling of object shape and appearance"
    - "what do single view 3d reconstruction networks learn"
    - "cerberus a multi headed derenderer"

Connected through hyperedge HE-2 (induced by source paper "multi garment net learning to dress 3d people from images"):
  with members:
    - "tex2shape detailed full human body geometry from a single image"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "pifu pixel aligned implicit function for high resolution clothed human digitization"
    - "learning based animation of clothing for virtual try on"
    - "simulcap single view human performance capture with cloth simulation"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "instance level human parsing via part grouping network"
    - "total capture a 3d deformation model for tracking faces hands and bodies"

Connected through hyperedge HE-3 (induced by source paper "geonet deep geodesic networks for point cloud analysis"):
  with members:
    - "dense depth posterior ddp from single image and sparse range"
    - "bilateral cyclic constraint and adaptive regularization for unsupervised monocular depth prediction"
    - "deep rbfnet point cloud feature learning using radial basis functions"
    - "conditional prior networks for optical flow"
    - "geo supervised visual depth prediction"
    - "pcpnet learning local shape properties from raw point clouds"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"

Connected through hyperedge HE-4 (induced by source paper "structural design using laplacian shells"):
  with members:
    - "two scale topology optimization with microstructures"
    - "from 3d models to 3d prints an overview of the processing pipeline"
    - "lightweight structure design under force location uncertainty"
    - "proportional topology optimization a new non sensitivity method for solving stress constrained and minimum compliance problems and its implementation in matlab"

Connected through hyperedge HE-5 (induced by source paper "a smoothness energy without boundary distortion for curved surfaces"):
  with members:
    - "the vector heat method"
    - "plan3d viewpoint and trajectory optimization for aerial multi view stereo reconstruction"
    - "functional characterization of deformation fields"
    - "natural boundary conditions for smoothing in geometry processing"

Connected through hyperedge HE-6 (induced by source paper "deep eikonal solvers"):
  with members:
    - "fast asymmetric fronts propagation for image segmentation"
    - "deep sets"
    - "pointnet deep learning on point sets for 3d classification and segmentation"

Connected through hyperedge HE-7 (induced by source paper "a minimalistic approach for fast computation of geodesic distances on triangular meshes"):
  with members:
    - "parallel and scalable heat methods for geodesic distance computation"
    - "spectrometer amortized sublinear spectral approximation of distance on graphs"

Connected through hyperedge HE-8 (induced by source paper "geometric shape features extraction using a steady state partial differential equation system"):
  with members:
    - "an axis based representation for recognition"
    - "disconnected skeleton shape at its absolute scale"

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.GR(Graphics)