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: "geodesic distance descriptors"
  Abstract: The Gromov-Hausdorff (GH) distance is traditionally used for measuring distances between metric spaces. It is defined as the minimal distortion of embedding one surface into the other, while the optimal correspondence can be described as the map that minimizes this distortion. Solving such a minimization is a hard combinatorial problem that requires pre-computation and storing of all pairwise geodesic distances for the matched surfaces. A popular way for compact representation of functions on surfaces is by projecting them into the leading eigenfunctions of the Laplace-Beltrami Operator (LBO). When truncated, The basis of the LBO is known to be the optimal for representing functions with bounded gradient in a min-max sense. Methods such as Spectral-GMDS exploit this idea to simplify and efficiently approximate a minimization related to the GH distance by operating in the truncated spectral domain, and obtain state of the art results for matching of nearly isometric shapes. However, when considering only a specific set of functions on the surface, such as geodesic distances, an optimized basis could be considered as an even better alternative. Here, we define the geodesic distance basis, which is optimal for compact approximation of geodesic distances, in terms of Frobenius norm. We use the suggested basis to extract the Geodesic Distance Descriptor (GDD), which encodes the geodesic distances information as a linear combination of the basis functions. We then show how these ideas can be used to efficiently and accurately approximate the metric spaces matching problem with almost no loss of information. These observations are used to construct a very simple and efficient procedure for shape correspondence. Experimental results show that the GDD improves both accuracy and efficiency of state of the art shape matching procedures.

Connected through hyperedge HE-1 (induced by source paper "deep manifold embedding for hyperspectral image classification"):
  with members:
    - "diversity in machine learning"
    - "geometric understanding of deep learning"
    - "rethinking feature distribution for loss functions in image classification"
    - "mining on manifolds metric learning without labels"
    - "image reconstruction by domain transform manifold learning"
    - "deep class wise hashing semantics preserving hashing via class wise loss"
    - "going deeper with contextual cnn for hyperspectral image classification"
    - "sphereface deep hypersphere embedding for face recognition"

Connected through hyperedge HE-2 (induced by source paper "functional maps representation on product manifolds"):
  with members:
    - "deep functional maps structured prediction for dense shape correspondence"
    - "composition operators matrix representation and the finite section method a theoretical framework for maps between shapes"
    - "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"
    - "geometric deep learning going beyond euclidean data"
    - "localized manifold harmonics for spectral shape analysis"
    - "hamiltonian operator for spectral shape analysis"
    - "efficient globally optimal 2d to 3d deformable shape matching"
    - "functional correspondence by matrix completion"

Connected through hyperedge HE-3 (induced by source paper "self functional maps"):
  with members:
    - "steklov spectral geometry for extrinsic shape analysis"
    - "on the optimality of shape and data representation in the spectral domain"
    - "spectral generalized multi dimensional scaling"
    - "smi 2011 full paper diffusion geometric maximally stable component detection in deformable shapes"
    - "discrete schemes for gaussian curvature and their convergence"

Connected through hyperedge HE-4 (induced by source paper "efficient inter geodesic distance computation and fast classical scaling"):
  with members:
    - "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-5 (induced by source paper "efficient deformable shape correspondence via kernel matching"):
  with members:
    - "deep functional maps structured prediction for dense shape correspondence"
    - "learning shape correspondence with anisotropic convolutional neural networks"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "geodesic convolutional neural networks on riemannian manifolds"
    - "functional correspondence by matrix completion"
    - "spectral descriptors for graph matching"
    - "spectral generalized multi dimensional scaling"
    - "coupled quasi harmonic bases"

Connected through hyperedge HE-6 (induced by source paper "fast classical scaling"):
  with members:
    - "natural boundary conditions for smoothing in geometry processing"
    - "geodesics in heat a new approach to computing distance based on heat flow"

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.CG(Computational Geometry)