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: "approximation algorithm for sparsest k partitioning"
  Abstract: Given a graph $G$, the sparsest-cut problem asks to find the set of vertices $S$ which has the least expansion defined as $$\phi_G(S) := \frac{w(E(S,\bar{S}))}{\min \set{w(S), w(\bar{S})}}, $$ where $w$ is the total edge weight of a subset. Here we study the natural generalization of this problem: given an integer $k$, compute a $k$-partition $\set{P_1, \ldots, P_k}$ of the vertex set so as to minimize $$ \phi_k(\set{P_1, \ldots, P_k}) := \max_i \phi_G(P_i). $$ Our main result is a polynomial time bi-criteria approximation algorithm which outputs a $(1 - \e)k$-partition of the vertex set such that each piece has expansion at most $O_{\varepsilon}(\sqrt{\log n \log k})$ times $OPT$. We also study balanced versions of this problem.

Connected through hyperedge HE-1 (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-2 (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"
    - "improved cheeger s inequality analysis of spectral partitioning algorithms through higher order spectral gap"
    - "many sparse cuts via higher eigenvalues"

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"
    - "improved cheeger s inequality and analysis of local graph partitioning using vertex expansion and expansion profile"
    - "partitioning well clustered graphs spectral clustering works"
    - "hypergraph markov operators eigenvalues and approximation algorithms"

Connected through hyperedge HE-4 (induced by source paper "spectral properties of hypergraph laplacian and approximation algorithms"):
  with members:
    - "spectral properties of laplacian and stochastic diffusion process for edge expansion in hypergraphs"
    - "approximation algorithms for hypergraph small set expansion and small set vertex expansion"
    - "hypergraph markov operators eigenvalues and approximation algorithms"
    - "the complexity of approximating vertex expansion"
    - "improved cheeger s inequality analysis of spectral partitioning algorithms through higher order spectral gap"
    - "many sparse cuts via higher eigenvalues"
    - "reductions between expansion problems"
    - "most tensor problems are np hard"

Connected through hyperedge HE-5 (induced by source paper "how to round subspaces a new spectral clustering algorithm"):
  with members:
    - "spectral clustering via the power method provably"
    - "improved cheeger s inequality analysis of spectral partitioning algorithms through higher order spectral gap"
    - "improved spectral norm bounds for clustering"
    - "many sparse cuts via higher eigenvalues"
    - "min max graph partitioning and small set expansion"
    - "optimal column based low rank matrix reconstruction"
    - "clustering with spectral norm and the k means algorithm"
    - "efficient volume sampling for row column subset selection"

Connected through hyperedge HE-6 (induced by source paper "partitioning well clustered graphs spectral clustering works"):
  with members:
    - "a note on spectral clustering"
    - "correlation clustering with noisy partial information"
    - "an almost linear time algorithm for approximate max flow in undirected graphs and its multicommodity generalizations"
    - "partitioning into expanders"
    - "improved cheeger s inequality analysis of spectral partitioning algorithms through higher order spectral gap"
    - "many sparse cuts via higher eigenvalues"
    - "faster spectral sparsification and numerical algorithms for sdd matrices"
    - "center based clustering under perturbation stability"

Connected through hyperedge HE-7 (induced by source paper "partitioning well clustered graphs with k means and heat kernel"):
  with members:
    - "an almost linear time algorithm for approximate max flow in undirected graphs and its multicommodity generalizations"
    - "partitioning into expanders"
    - "improved cheeger s inequality analysis of spectral partitioning algorithms through higher order spectral gap"
    - "many sparse cuts via higher eigenvalues"
    - "faster spectral sparsification and numerical algorithms for sdd matrices"
    - "reductions between expansion problems"
    - "spectral sparsification of graphs"
    - "graph sparsification by effective resistances"

Connected through hyperedge HE-8 (induced by source paper "hypergraph markov operators eigenvalues and approximation algorithms"):
  with members:
    - "approximation algorithms for hypergraph small set expansion and small set vertex expansion"
    - "the complexity of approximating vertex expansion"
    - "many sparse cuts via higher eigenvalues"
    - "min max graph partitioning and small set expansion"
    - "most tensor problems are np hard"
    - "random tensors and planted cliques"

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)