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 "spectral sparsification of simplicial complexes for clustering and label propagation"):
  Abstract: As a generalization of the use of graphs to describe pairwise interactions, simplicial complexes can be used to model higher-order interactions between three or more objects in complex systems. There has been a recent surge in activity for the development of data analysis methods applicable to simplicial complexes, including techniques based on computational topology, higher-order random processes, generalized Cheeger inequalities, isoperimetric inequalities, and spectral methods. In particular, spectral learning methods (e.g. label propagation and clustering) that directly operate on simplicial complexes represent a new direction for analyzing such complex datasets. #R##N#To apply spectral learning methods to massive datasets modeled as simplicial complexes, we develop a method for sparsifying simplicial complexes that preserves the spectrum of the associated Laplacian matrices. We show that the theory of Spielman and Srivastava for the sparsification of graphs extends to simplicial complexes via the up Laplacian. In particular, we introduce a generalized effective resistance for simplices, provide an algorithm for sparsifying simplicial complexes at a fixed dimension, and give a specific version of the generalized Cheeger inequality for weighted simplicial complexes. Finally, we introduce higher-order generalizations of spectral clustering and label propagation for simplicial complexes and demonstrate via experiments the utility of the proposed spectral sparsification method for these applications.

Includes members:
  Member "near optimal sketching of low rank tensor regression":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multiresolution tensor learning for efficient and interpretable spatial analysis")
  Member "polynomial sized topological approximations using the permutahedron":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "delaunay simplices in diagonally distorted lattices")
  Member "tensor spectral clustering for partitioning higher order network structures":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pasta a parallel sparse tensor algorithm benchmark suite")
  Member "pagerank beyond the web":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "nonlinearity networks a 2020 vision")
  Member "graph induced complex on point data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "topological data analysis with epsilon net induced lazy witness complex")
  Member "a simple combinatorial algorithm for solving sdd systems in nearly linear time":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "circulation control for faster minimum cost flow in unit capacity graphs")
  Member "computing topological persistence for simplicial maps":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gpu accelerated computation of vietoris rips persistence barcodes")
  Member "applications of zigzag persistence to topological data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "persistent homology over directed acyclic 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.CG(Computational Geometry)