human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "spectrometer amortized sublinear spectral approximation of distance on graphs"):
  Abstract: We present a method to approximate pairwise distance on a graph, having an amortized sub-linear complexity in its size. The proposed method follows the so called heat method due to Crane et al. The only additional input are the values of the eigenfunctions of the graph Laplacian at a subset of the vertices. Using these values we estimate a random walk from the source points, and normalize the result into a unit gradient function. The eigenfunctions are then used to synthesize distance values abiding by these constraints at desired locations. We show that this method works in practice on different types of inputs ranging from triangular meshes to general graphs. We also demonstrate that the resulting approximate distance is accurate enough to be used as the input to a recent method for intrinsic shape correspondence computation.

Includes members:
  Member "bayesian inference of bijective non rigid shape correspondence":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "geometric deep learning on graphs and manifolds using mixture model cnns")
  Member "point wise map recovery and refinement from functional correspondence":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "smooth shells multi scale shape registration with functional maps")
  Member "on the optimality of shape and data representation in the spectral domain":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bilateral operators for functional maps")
  Member "geodesics in heat a new approach to computing distance based on heat flow":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "nasa neural articulated shape approximation")
  Member "spectral generalized multi dimensional scaling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "efficient and robust shape correspondence via sparsity enforced quadratic assignment")
  Member "a correspondence less approach to matching of deformable shapes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mumford shah mesh processing using the ambrosio tortorelli functional")
  Member "a tutorial on spectral clustering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "approximating spectral clustering via sampling a review")

Question: Please tell me which class the source paper 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.DS(Data Structures and Algorithms)