human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "improved cheeger s inequality and analysis of local graph partitioning using vertex expansion and expansion profile"
  Abstract: We prove two generalizations of the Cheeger's inequality. The first generalization relates the second eigenvalue to the edge expansion and the vertex expansion of the graph G, $\lambda_2 = \Omega(\phi^V(G) \phi(G))$, where $\phi^V(G)$ denotes the robust vertex expansion of G and $\phi(G)$ denotes the edge expansion of G. The second generalization relates the second eigenvalue to the edge expansion and the expansion profile of G, for all $k \ge 2$, $\lambda_2 = \Omega(\phi_k(G) \phi(G) / k)$, where $\phi_k(G)$ denotes the k-way expansion of G. These show that the spectral partitioning algorithm has better performance guarantees when $\phi^V(G)$ is large (e.g. planted random instances) or $\phi_k(G)$ is large (instances with few disjoint non-expanding sets). Both bounds are tight up to a constant factor. #R##N#Our approach is based on a method to analyze solutions of Laplacian systems, and this allows us to extend the results to local graph partitioning algorithms. In particular, we show that our approach can be used to analyze personal pagerank vectors, and to give a local graph partitioning algorithm for the small-set expansion problem with performance guarantees similar to the generalizations of Cheeger's inequality. We also present a spectral approach to prove similar results for the truncated random walk algorithm. These show that local graph partitioning algorithms almost match the performance of the spectral partitioning algorithm, with the additional advantages that they apply to the small-set expansion problem and their running time could be sublinear. Our techniques provide common approaches to analyze the spectral partitioning algorithm and local graph partitioning algorithms.

Connected through hyperedge HE-1 (induced by source paper "generalizing the hypergraph laplacian via a diffusion process with mediators"):
  with members:
    - "correlation clustering with noisy partial information"
    - "partitioning well clustered graphs spectral clustering works"
    - "hypergraph markov operators eigenvalues and approximation algorithms"
    - "approximation algorithm for sparsest k partitioning"
    - "improved cheeger s inequality analysis of spectral partitioning algorithms through higher order spectral gap"
    - "many sparse cuts via higher eigenvalues"

Connected through hyperedge HE-2 (induced by source paper "cheeger inequalities for submodular transformations"):
  with members:
    - "finding cheeger cuts in hypergraphs via heat equation"
    - "polynomial time algorithms for submodular laplacian systems"
    - "submodular hypergraphs p laplacians cheeger inequalities and spectral clustering"
    - "inhomogoenous hypergraph clustering with applications"
    - "spectral properties of hypergraph laplacian and approximation algorithms"
    - "heat kernel based community detection"
    - "provable submodular minimization using wolfe s algorithm"
    - "hypergraph markov operators eigenvalues and approximation algorithms"

Connected through hyperedge HE-3 (induced by source paper "diffusion operator and spectral analysis for directed hypergraph laplacian"):
  with members:
    - "spectral properties of hypergraph laplacian and approximation algorithms"
    - "partitioning well clustered graphs spectral clustering works"
    - "hypergraph markov operators eigenvalues and approximation algorithms"
    - "approximation algorithm for sparsest k partitioning"

Connected through hyperedge HE-4 (induced by source paper "parallel local graph clustering"):
  with members:
    - "a simple and strongly local flow based method for cut improvement"
    - "distributed algorithms for finding local clusters using heat kernel pagerank"
    - "think locally act locally detection of small medium sized and large communities in large networks"
    - "computing heat kernel pagerank and a local clustering algorithm"
    - "heat kernel based community detection"
    - "flow based algorithms for local graph clustering"
    - "approximating the expansion profile and almost optimal local graph clustering"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-5 (induced by source paper "random walks and evolving sets faster convergences and limitations"):
  with members:
    - "analysis of boolean functions"
    - "local graph clustering beyond cheeger s inequality"
    - "improved cheeger s inequality analysis of spectral partitioning algorithms through higher order spectral gap"
    - "on extracting common random bits from correlated sources on large alphabets"
    - "a local clustering algorithm for massive graphs and its application to nearly linear time graph partitioning"
    - "finding sparse cuts locally using evolving sets"
    - "max cut and the smallest eigenvalue"

Question: Please tell me which class the center node 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)