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 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.

Connected through hyperedge HE-1 (induced by source paper "efficient algorithms for the potts model on small set expanders"):
  with members:
    - "counting independent sets in unbalanced bipartite graphs"
    - "fast algorithms at low temperatures via markov chains"
    - "statistical physics approaches to unique games"
    - "algorithms for bis hard problems on expander graphs"
    - "algorithmic pirogov sinai theory"
    - "partitioning into expanders"
    - "ferromagnetic potts model refined bis hardness and related results"
    - "approximating the expansion profile and almost optimal local graph clustering"

Connected through hyperedge HE-2 (induced by source paper "planted models for k way edge and vertex expansion"):
  with members:
    - "semi random graphs with planted sparse vertex cuts algorithms for exact and approximate recovery"
    - "multisection in the stochastic block model using semidefinite programming"
    - "learning communities in the presence of errors"
    - "how robust are reconstruction thresholds for community detection"
    - "partitioning well clustered graphs spectral clustering works"
    - "constant factor approximation for balanced cut in the pie model"
    - "approximation algorithms for hypergraph small set expansion and small set vertex expansion"
    - "exact recovery in the stochastic block model"

Connected through hyperedge HE-3 (induced by source paper "convex programming based spectral clustering"):
  with members:
    - "emnist an extension of mnist to handwritten letters"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "spectral clustering by ellipsoid and its connection to separable nonnegative matrix factorization"
    - "a note on spectral clustering"
    - "partitioning well clustered graphs spectral clustering works"
    - "partitioning into expanders"
    - "defining and evaluating network communities based on ground truth"
    - "computing a nonnegative matrix factorization provably"

Connected through hyperedge HE-4 (induced by source paper "generalizing the hypergraph laplacian via a diffusion process with mediators"):
  with members:
    - "improved cheeger s inequality and analysis of local graph partitioning using vertex expansion and expansion profile"
    - "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"
    - "many sparse cuts via higher eigenvalues"

Connected through hyperedge HE-5 (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"
    - "improved cheeger s inequality and analysis of local graph partitioning using vertex expansion and expansion profile"
    - "heat kernel based community detection"
    - "provable submodular minimization using wolfe s algorithm"

Connected through hyperedge HE-6 (induced by source paper "capacity releasing diffusion for speed and locality"):
  with members:
    - "parallel local graph clustering"
    - "think locally act locally detection of small medium sized and large communities in large networks"
    - "pagerank beyond the web"
    - "many sparse cuts via higher eigenvalues"
    - "social structure of facebook networks"
    - "approximating the exponential the lanczos method and an tilde o m time spectral algorithm for balanced separator"
    - "finding sparse cuts locally using evolving sets"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-7 (induced by source paper "approximating the spectrum of a graph"):
  with members:
    - "learning populations of parameters"
    - "spectrum estimation from samples"
    - "sketching as a tool for numerical linear algebra"
    - "a quasi polynomial time partition oracle for graphs with an excluded minor"
    - "many sparse cuts via higher eigenvalues"

Connected through hyperedge HE-8 (induced by source paper "approximating sparsest cut in low rank graphs via embeddings from approximately low dimensional spaces"):
  with members:
    - "guruswami sinop rounding without higher level lasserre"
    - "improved arv rounding in small set expanders and graphs of bounded threshold rank"
    - "a prg for lipschitz functions of polynomials with applications to sparsest cut"
    - "approximating non uniform sparsest cut via generalized spectra"

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)