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 "on solving linear systems in sublinear time"):
  Abstract: We study \emph{sublinear} algorithms that solve linear systems locally. In the classical version of this problem the input is a matrix $S\in \mathbb{R}^{n\times n}$ and a vector $b\in\mathbb{R}^n$ in the range of $S$, and the goal is to output $x\in \mathbb{R}^n$ satisfying $Sx=b$. For the case when the matrix $S$ is symmetric diagonally dominant (SDD), the breakthrough algorithm of Spielman and Teng [STOC 2004] approximately solves this problem in near-linear time (in the input size which is the number of non-zeros in $S$), and subsequent papers have further simplified, improved, and generalized the algorithms for this setting. #R##N#Here we focus on computing one (or a few) coordinates of $x$, which potentially allows for sublinear algorithms. Formally, given an index $u\in [n]$ together with $S$ and $b$ as above, the goal is to output an approximation $\hat{x}_u$ for $x^*_u$, where $x^*$ is a fixed solution to $Sx=b$. #R##N#Our results show that there is a qualitative gap between SDD matrices and the more general class of positive semidefinite (PSD) matrices. For SDD matrices, we develop an algorithm that approximates a single coordinate $x_{u}$ in time that is polylogarithmic in $n$, provided that $S$ is sparse and has a small condition number (e.g., Laplacian of an expander graph). The approximation guarantee is additive $| \hat{x}_u-x^*_u | \le \epsilon \| x^* \|_\infty$ for accuracy parameter $\epsilon>0$. We further prove that the condition-number assumption is necessary and tight. #R##N#In contrast to the SDD matrices, we prove that for certain PSD matrices $S$, the running time must be at least polynomial in $n$. This holds even when one wants to obtain the same additive approximation, and $S$ has bounded sparsity and condition number.

Includes members:
  Member "a quantum inspired classical algorithm for recommendation systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "robust and sample optimal algorithms for psd low rank approximation")
  Member "powers of tensors and fast matrix multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "new algorithms and hardness for incremental single source shortest paths in directed graphs")
  Member "probabilistic spectral sparsification in sublinear time":
    also connected through other hyperedges:
      (none)
  Member "sublinear algorithms for local graph centrality estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "walking randomly massively and efficiently")
  Member "fast local computation algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "local access to huge random objects through partial sampling")
  Member "finding sparse cuts locally using evolving sets":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deterministic graph cuts in subquadratic time sparse balanced and k vertex")
  Member "nearly linear time algorithms for graph partitioning graph sparsification and solving linear systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "compressive sensing for cut improvement and local clustering")

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)