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: "the vector heat method"
  Abstract: This paper describes a method for efficiently computing parallel transport of tangent vectors on curved surfaces, or more generally, any vector-valued data on a curved manifold. More precisely, it extends a vector field defined over any region to the rest of the domain via parallel transport along shortest geodesics. This basic operation enables fast, robust algorithms for extrapolating level set velocities, inverting the exponential map, computing geometric medians and Karcher/Fr\'echet means of arbitrary distributions, constructing centroidal Voronoi diagrams, and finding consistently ordered landmarks. Rather than evaluate parallel transport by explicitly tracing geodesics, we show that it can be computed via a short-time heat flow involving the connection Laplacian. As a result, transport can be achieved by solving three prefactored linear systems, each akin to a standard Poisson problem. Moreover, to implement the method we need only a discrete connection Laplacian, which we describe for a variety of geometric data structures (point clouds, polygon meshes, etc.). We also study the numerical behavior of our method, showing empirically that it converges under refinement, and augment the construction of intrinsic Delaunay triangulations (iDT) so that they can be used in the context of tangent vector field processing.

Connected through hyperedge HE-1 (induced by source paper "horizontal flows and manifold stochastics in geometric deep learning"):
  with members:
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "covariance in physics and convolutional neural networks"
    - "parallel transport convolution a new tool for convolutional neural networks on manifolds"
    - "multi directional geodesic neural networks via equivariant convolution"
    - "differential geometry and stochastic dynamics with deep learning numerics"
    - "geometric deep learning going beyond euclidean data"
    - "learning shape correspondence with anisotropic convolutional neural networks"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"

Connected through hyperedge HE-2 (induced by source paper "gaussians on riemannian manifolds for robot learning and adaptive control"):
  with members:
    - "rmpflow a computational graph for automatic motion policy generation"
    - "geometry aware manipulability learning tracking and transfer"
    - "a micro lie theory for state estimation in robotics"
    - "on manifold preintegration for real time visual inertial odometry"
    - "identification of fully physical consistent inertial parameters using optimization on manifolds"
    - "trajectory synthesis for fisher information maximization"

Connected through hyperedge HE-3 (induced by source paper "a smoothness energy without boundary distortion for curved surfaces"):
  with members:
    - "plan3d viewpoint and trajectory optimization for aerial multi view stereo reconstruction"
    - "functional characterization of deformation fields"
    - "natural boundary conditions for smoothing in geometry processing"
    - "geodesics in heat a new approach to computing distance based on heat flow"

Connected through hyperedge HE-4 (induced by source paper "parallel and scalable heat methods for geodesic distance computation"):
  with members:
    - "parallel chen han pch algorithm for discrete geodesics"
    - "geodesics in heat a new approach to computing distance based on heat flow"
    - "dynamic generators of topologically embedded graphs"

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)