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 "improved cheeger s inequality analysis of spectral partitioning algorithms through higher order spectral gap"):
  Abstract: Let \phi(G) be the minimum conductance of an undirected graph G, and let 0=\lambda_1  = 2, #R##N#\phi(G) = O(k) \lambda_2 / \sqrt{\lambda_k}, and this performance guarantee is achieved by the spectral partitioning algorithm. This improves Cheeger's inequality, and the bound is optimal up to a constant factor for any k. Our result shows that the spectral partitioning algorithm is a constant factor approximation algorithm for finding a sparse cut if \lambda_k$ is a constant for some constant k. This provides some theoretical justification to its empirical performance in image segmentation and clustering problems. We extend the analysis to other graph partitioning problems, including multi-way partition, balanced separator, and maximum cut.

Includes members:
  Member "clustering is difficult only when it does not matter":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the unreasonable effectiveness of the greedy algorithm greedy adapts to sharpness")
  Member "on the practically interesting instances of maxcut":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bilu linial stability certified algorithms and the independent set problem")
  Member "many sparse cuts via higher eigenvalues":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "planted models for k way edge and vertex expansion")
  Member "faster sdp hierarchy solvers for local rounding algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximating constraint satisfaction problems on high dimensional expanders")
  Member "approximating the expansion profile and almost optimal local graph clustering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "efficient algorithms for the potts model on small set expanders")
  Member "approximating non uniform sparsest cut via generalized spectra":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "matrix decompositions and sparse graph regularity")
  Member "empirical comparison of algorithms for network community detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "certified robustness of community detection against adversarial structural perturbation via randomized smoothing")
  Member "are stable instances easy":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the unreasonable effectiveness of the greedy algorithm greedy adapts to sharpness")

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)