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 "towards scalable spectral sparsification of directed graphs"):
  Abstract: Recent spectral graph sparsification research allows constructing nearly-linear-sized subgraphs that can well preserve the spectral (structural) properties of the original graph, such as the the first few eigenvalues and eigenvectors of the graph Laplacian, leading to the development of a variety of nearly-linear time numerical and graph algorithms. However, there is not a unified approach that allows for truly-scalable spectral sparsification of both directed and undirected graphs. For the first time, this paper proves the existence of linear-sized spectral sparsifiers for general directed graphs, and introduces a practically-efficient yet unified spectral graph sparsification approach that allows sparsifying real-world, large-scale directed and undirected graphs with guaranteed preservation of the original graph spectra. By exploiting a highly-scalable (nearly-linear complexity) spectral matrix perturbation analysis framework for constructing nearly-linear sized (directed) subgraphs, it enables to well preserve the key eigenvalues and eigenvectors of the original (directed) graph Laplacians. Compared with prior works that are limited to only strongly-connected directed graphs, the proposed approach is more general and thus will allow for truly-scalable spectral sparsification of a much wider range of real-world complex graphs. The proposed method has been validated using various kinds of directed graphs obtained from public domain sparse matrix collections, showing promising spectral sparsification and partitioning results for general directed graphs.

Includes members:
  Member "graph convolutional neural networks for web scale recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on the expressive power of graph neural networks")
  Member "solving directed laplacian systems in nearly linear time through sparse lu factorizations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "spectral sparsification via bounded independence sampling")
  Member "similarity aware spectral sparsification by edge filtering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "graphzoom a multi level spectral approach for accurate and scalable graph embedding")
  Member "an sdp based algorithm for linear sized spectral sparsification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a spectral approach to network design")
  Member "almost linear time algorithms for markov chains and new spectral primitives for directed graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "flows in almost linear time via adaptive preconditioning")
  Member "twice ramanujan sparsifiers":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sketching transformed matrices with applications to natural language processing")
  Member "spectral sparsification of graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "graph sparsification by effective resistances":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)