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: "thingi10k a dataset of 10 000 3d printing models"
  Abstract: Empirically validating new 3D-printing related algorithms and implementations requires testing data representative of inputs encountered \emph{in the wild}. An ideal benchmarking dataset should not only draw from the same distribution of shapes people print in terms of class (e.g., toys, mechanisms, jewelry), representation type (e.g., triangle soup meshes) and complexity (e.g., number of facets), but should also capture problems and artifacts endemic to 3D printing models (e.g., self-intersections, non-manifoldness). We observe that the contextual and geometric characteristics of 3D printing models differ significantly from those used for computer graphics applications, not to mention standard models (e.g., Stanford bunny, Armadillo, Fertility). We present a new dataset of 10,000 models collected from an online 3D printing model-sharing database. Via analysis of both geometric (e.g., triangle aspect ratios, manifoldness) and contextual (e.g., licenses, tags, classes) characteristics, we demonstrate that this dataset represents a more concise summary of real-world models used for 3D printing compared to existing datasets. To facilitate future research endeavors, we also present an online query interface to select subsets of the dataset according to project-specific characteristics. The complete dataset and per-model statistical data are freely available to the public.

Connected through hyperedge HE-1 (induced by source paper "deep differentiable grasp planner for high dof grippers"):
  with members:
    - "generating grasp poses for a high dof gripper using neural networks"
    - "new formulation of mixed integer conic programming for globally optimal grasp planning"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "difftaichi differentiable programming for physical simulation"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "dexterous manipulation with deep reinforcement learning efficient general and low cost"
    - "neural kinematic networks for unsupervised motion retargetting"
    - "chainqueen a real time differentiable physical simulator for soft robotics"

Connected through hyperedge HE-2 (induced by source paper "obtaining a canonical polygonal schema from a greedy homotopy basis with minimal mesh refinement"):
  with members:
    - "dynamic generators of topologically embedded graphs"
    - "optimally cutting a surface into a disk"

Connected through hyperedge HE-3 (induced by source paper "prs net planar reflective symmetry detection net for 3d models"):
  with members:
    - "robust watertight manifold surface generation method for shapenet models"
    - "abc a big cad model dataset for geometric deep learning"
    - "learning shape abstractions by assembling volumetric primitives"
    - "learning a predictable and generative vector representation for objects"
    - "octnet learning deep 3d representations at high resolutions"
    - "shapenet an information rich 3d model repository"
    - "adam a method for stochastic optimization"
    - "3d shapenets a deep representation for volumetric shapes"

Connected through hyperedge HE-4 (induced by source paper "spectral coarsening of geometric operators"):
  with members:
    - "deep functional maps structured prediction for dense shape correspondence"
    - "emnist an extension of mnist to handwritten letters"
    - "geometric deep learning going beyond euclidean data"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "efficient deformable shape correspondence via kernel matching"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "approximate gaussian elimination for laplacians fast sparse and simple"

Connected through hyperedge HE-5 (induced by source paper "cubic stylization"):
  with members:
    - "semi supervised co analysis of 3d shape styles from projected lines"
    - "anderson acceleration for geometry optimization and physics simulation"
    - "static dynamic filtering for mesh geometry"
    - "natural boundary conditions for smoothing in geometry processing"

Connected through hyperedge HE-6 (induced by source paper "computing three dimensional constrained delaunay refinement using the gpu"):
  with members:
    - "one machine one minute three billion tetrahedra"

Connected through hyperedge HE-7 (induced by source paper "fast tetrahedral meshing in the wild"):
  with members:
    - "error bounded and feature preserving surface remeshing with minimal angle improvement"

Connected through hyperedge HE-8 (induced by source paper "a large scale comparison of tetrahedral and hexahedral elements for finite element analysis"):
  with members:
    - "poly spline finite element method"

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)