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 properties of hypergraph laplacian and approximation algorithms"):
  Abstract: The celebrated Cheeger's Inequality establishes a bound on the edge expansion of a graph via its spectrum. This inequality is central to a rich spectral theory of graphs, based on studying the eigenvalues and eigenvectors of the adjacency matrix (and other related matrices) of graphs. It has remained open to define a suitable spectral model for hypergraphs whose spectra can be used to estimate various combinatorial properties of the hypergraph. #R##N#In this paper we introduce a new hypergraph Laplacian operator generalizing the Laplacian matrix of graphs. In particular, the operator is induced by a diffusion process on the hypergraph, such that within each hyperedge, measure flows from vertices having maximum weighted measure to those having minimum. Since the operator is non-linear, we have to exploit other properties of the diffusion process to recover a spectral property concerning the "second eigenvalue" of the resulting Laplacian. Moreover, we show that higher order spectral properties cannot hold in general using the current framework. #R##N#We consider a stochastic diffusion process, in which each vertex also experiences Brownian noise from outside the system. We show a relationship between the second eigenvalue and the convergence behavior of the process. #R##N#We show that various hypergraph parameters like multi-way expansion and diameter can be bounded using this operator's spectral properties. Since higher order spectral properties do not hold for the Laplacian operator, we instead use the concept of procedural minimizers to consider higher order Cheeger-like inequalities. For any positive integer $k$, we give a polynomial time algorithm to compute an $O(\log r)$-approximation to the $k$-th procedural minimizer, where $r$ is the maximum cardinality of a hyperedge. We show that this approximation factor is optimal under the SSE hypothesis for constant values of $k$.

Includes members:
  Member "spectral properties of laplacian and stochastic diffusion process for edge expansion in hypergraphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "curvature of hypergraphs via multi marginal optimal transport")
  Member "approximation algorithms for hypergraph small set expansion and small set vertex expansion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "planted models for k way edge and vertex expansion")
  Member "hypergraph markov operators eigenvalues and approximation algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hypergcn a new method of training graph convolutional networks on hypergraphs")
  Member "the complexity of approximating vertex expansion":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "planted models for k way edge and vertex expansion")
  Member "approximation algorithm for sparsest k partitioning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "planted models for k way edge and vertex expansion")
  Member "improved cheeger s inequality analysis of spectral partitioning algorithms through higher order spectral gap":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient algorithms for the potts model on small set expanders")
  Member "many sparse cuts via higher eigenvalues":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "planted models for k way edge and vertex expansion")
  Member "reductions between expansion problems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "bisect and conquer hierarchical clustering via max uncut bisection")

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.DM(Discrete Mathematics)