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 hypergraphs"):
  Abstract: For an undirected/directed hypergraph $G=(V,E)$, its Laplacian $L_G\colon\mathbb{R}^V\to \mathbb{R}^V$ is defined such that its ``quadratic form'' $\boldsymbol{x}^\top L_G(\boldsymbol{x})$ captures the cut information of $G$. In particular, $\boldsymbol{1}_S^\top L_G(\boldsymbol{1}_S)$ coincides with the cut size of $S \subseteq V$, where $\boldsymbol{1}_S \in \mathbb{R}^V$ is the characteristic vector of $S$. A weighted subgraph $H$ of a hypergraph $G$ on a vertex set $V$ is said to be an $\epsilon$-spectral sparsifier of $G$ if $(1-\epsilon)\boldsymbol{x}^\top L_H(\boldsymbol{x}) \leq \boldsymbol{x}^\top L_G(\boldsymbol{x}) \leq (1+\epsilon)\boldsymbol{x}^\top L_H(\boldsymbol{x})$ holds for every $\boldsymbol{x} \in \mathbb{R}^V$. In this paper, we present a polynomial-time algorithm that, given an undirected/directed hypergraph $G$ on $n$ vertices, constructs an $\epsilon$-spectral sparsifier of $G$ with $O(n^3\log n/\epsilon^2)$ hyperedges/hyperarcs. The proposed spectral sparsification can be used to improve the time and space complexities of algorithms for solving problems that involve the quadratic form, such as computing the eigenvalues of $L_G$, computing the effective resistance between a pair of vertices in $G$, semi-supervised learning based on $L_G$, and cut problems on $G$. In addition, our sparsification result implies that any submodular function $f\colon 2^V \to \mathbb{R}_+$ with $f(\emptyset)=f(V)=0$ can be concisely represented by a directed hypergraph. Accordingly, we show that, for any distribution, we can properly and agnostically learn submodular functions $f\colon 2^V \to [0,1]$ with $f(\emptyset)=f(V)=0$, with $O(n^4\log (n/\epsilon) /\epsilon^4)$ samples.

Includes members:
  Member "cheeger inequalities for submodular transformations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "quadratic decomposable submodular function minimization theory and practice")
  Member "sketching cuts in graphs and hypergraphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "practice of streaming and dynamic graphs concepts models systems and parallelism")
  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 "representation approximation and learning of submodular functions using low rank decision trees":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximate mathbb f _2 sketching of valuation functions")
  Member "learning pseudo boolean k dnf and submodular functions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "approximate mathbb f _2 sketching of valuation functions")
  Member "submodular functions are noise stable":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "approximate mathbb f _2 sketching of valuation functions")
  Member "fast approximation algorithms for cut based problems in undirected graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a deterministic algorithm for balanced cut with applications to dynamic connectivity flows and beyond")
  Member "privately releasing conjunctions and the statistical query barrier":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "approximate mathbb f _2 sketching of valuation functions")

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)