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 "graph powering and spectral robustness"):
  Abstract: Spectral algorithms, such as principal component analysis and spectral clustering, typically require careful data transformations to be effective: upon observing a matrix $A$, one may look at the spectrum of $\psi(A)$ for a properly chosen $\psi$. The issue is that the spectrum of $A$ might be contaminated by non-informational top eigenvalues, e.g., due to scale` variations in the data, and the application of $\psi$ aims to remove these. #R##N#Designing a good functional $\psi$ (and establishing what good means) is often challenging and model dependent. This paper proposes a simple and generic construction for sparse graphs, $$\psi(A) = \1((I+A)^r \ge1),$$ where $A$ denotes the adjacency matrix and $r$ is an integer (less than the graph diameter). This produces a graph connecting vertices from the original graph that are within distance $r$, and is referred to as graph powering. It is shown that graph powering regularizes the graph and decontaminates its spectrum in the following sense: (i) If the graph is drawn from the sparse Erd\H{o}s-R\'enyi ensemble, which has no spectral gap, it is shown that graph powering produces a `maximal' spectral gap, with the latter justified by establishing an Alon-Boppana result for powered graphs; (ii) If the graph is drawn from the sparse SBM, graph powering is shown to achieve the fundamental limit for weak recovery (the KS threshold) similarly to \cite{massoulie-STOC}, settling an open problem therein. Further, graph powering is shown to be significantly more robust to tangles and cliques than previous spectral algorithms based on self-avoiding or nonbacktracking walk counts \cite{massoulie-STOC,Mossel_SBM2,bordenave,colin3}. This is illustrated on a geometric block model that is dense in cliques.

Includes members:
  Member "derandomization beyond connectivity undirected laplacian systems in nearly logarithmic space":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "spectral sparsification via bounded independence sampling")
  Member "the geometric block model":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "active learning in the geometric block model")
  Member "robust estimators in high dimensions without the computational intractability":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "locally private hypothesis selection")
  Member "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "randomized spectral clustering in large scale stochastic block models")
  Member "semidefinite programs on sparse random graphs and their application to community detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "computational hardness of certifying bounds on constrained pca problems")
  Member "how robust are reconstruction thresholds for community detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "improper learning for non stochastic control")
  Member "limitations in the spectral method for graph partitioning detectability threshold and localization of eigenvectors":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fragility of spectral clustering for networks with an overlapping structure")
  Member "spectral redemption in clustering sparse networks":
    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)