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 "graph reduction with spectral and cut guarantees"):
  Abstract: Can one reduce the size of a graph without significantly altering its basic properties? The graph reduction problem is hereby approached from the perspective of restricted spectral approximation, a modification of the spectral similarity measure used for graph sparsification. This choice is motivated by the observation that restricted approximation carries strong spectral and cut guarantees, and that it implies approximation results for unsupervised learning problems relying on spectral embeddings. #R##N#The paper then focuses on coarsening---the most common type of graph reduction. Sufficient conditions are derived for a small graph to approximate a larger one in the sense of restricted similarity. These findings give rise to nearly-linear algorithms that, compared to both standard and advanced graph reduction methods, find coarse graphs of improved quality, often by a large margin, without sacrificing speed.

Includes members:
  Member "analyzing inverse problems with invertible neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "latent normalizing flows for many to many cross domain mappings")
  Member "spectrally approximating large graphs with smaller graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "dynamic edge conditioned filters in convolutional neural networks on graphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "memory based graph networks")
  Member "geometric deep learning going beyond euclidean data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on deep geometry learning from a representation perspective")
  Member "a 2 ϵ approximation for maximum weight matching in the semi streaming model":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "two player hidden pointer chasing and multi pass lower bounds in turnstile streams")
  Member "convolutional neural networks on graphs with fast localized spectral filtering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on deep geometry learning from a representation perspective")
  Member "compressive spectral clustering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "a multiscale pyramid transform for graph signals":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "copt coordinated optimal transport on graphs")

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)