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 "random walks and evolving sets faster convergences and limitations"):
  Abstract: Analyzing the mixing time of random walks is a well-studied problem with applications in random sampling and more recently in graph partitioning. In this work, we present new analysis of random walks and evolving sets using more combinatorial graph structures, and show some implications in approximating small-set expansion. On the other hand, we provide examples showing the limitations of using random walks and evolving sets in disproving the small-set expansion hypothesis. #R##N#- We define a combinatorial analog of the spectral gap, and use it to prove the convergence of non-lazy random walks. A corollary is a tight lower bound on the small-set expansion of graph powers for any graph. #R##N#- We prove that random walks converge faster when the robust vertex expansion of the graph is larger. This provides an improved analysis of the local graph partitioning algorithm using the evolving set process. #R##N#- We give an example showing that the evolving set process fails to disprove the small-set expansion hypothesis. This refutes a conjecture of Oveis Gharan and shows the limitations of local graph partitioning algorithms in approximating small-set expansion.

Includes members:
  Member "improved cheeger s inequality and analysis of local graph partitioning using vertex expansion and expansion profile":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "generalizing the hypergraph laplacian via a diffusion process with mediators")
  Member "analysis of boolean functions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "coarse grained complexity for dynamic algorithms")
  Member "local graph clustering beyond cheeger s inequality":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "local network community detection with continuous optimization of conductance and weighted kernel k means")
  Member "improved cheeger s inequality analysis of spectral partitioning algorithms through higher order spectral gap":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient algorithms for the potts model on small set expanders")
  Member "on extracting common random bits from correlated sources on large alphabets":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "communication for generating correlation a unifying survey")
  Member "a local clustering algorithm for massive graphs and its application to nearly linear time graph partitioning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "compressive sensing for cut improvement and local clustering")
  Member "finding sparse cuts locally using evolving sets":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deterministic graph cuts in subquadratic time sparse balanced and k vertex")
  Member "max cut and the smallest eigenvalue":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adversarial perturbations of opinion dynamics in networks")

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)