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 "fast classical scaling"):
  Abstract: Multidimensional-scaling (MDS) is a dimensionality reduction tool used for information analysis, data visualization and manifold learning. Most MDS procedures find embedding of data points in low dimensional Euclidean (flat) domains, such that distances between the points are as close as possible to given inter-points dissimilarities. We present an efficient solver for Classical Scaling, a specific MDS model, by extrapolating the information provided by distances measured from a subset of the points to the rest. The computational and space complexities of the new MDS procedures are be thereby reduced from quadratic to quasi-linear in the number of data points. Incorporating both local and global information about the data allows us to construct a low rank approximation to the inter-geodesic distances between the data points. As a by-product, the proposed method allows for efficient computation of geodesic distances. Finally, we show how to apply our method to two geometric analysis applications and obtain state of the art results.

Includes members:
  Member "natural boundary conditions for smoothing in geometry processing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cubic stylization")
  Member "geodesic distance descriptors":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep manifold embedding for hyperspectral image classification")
  Member "geodesics in heat a new approach to computing distance based on heat flow":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "nasa neural articulated shape approximation")

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.CG(Computational Geometry)