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: "approximating non uniform sparsest cut via generalized spectra"
  Abstract: We give an approximation algorithm for non-uniform sparsest cut with the following guarantee: For any $\epsilon,\delta \in (0,1)$, given cost and demand graphs with edge weights $C, D$ respectively, we can find a set $T\subseteq V$ with $\frac{C(T,V\setminus T)}{D(T,V\setminus T)}$ at most $\frac{1+\epsilon}{\delta}$ times the optimal non-uniform sparsest cut value, in time $2^{r/(\delta\epsilon)}\poly(n)$ provided $\lambda_r \ge \Phi^*/(1-\delta)$. Here $\lambda_r$ is the $r$'th smallest generalized eigenvalue of the Laplacian matrices of cost and demand graphs; $C(T,V\setminus T)$ (resp. $D(T,V\setminus T)$) is the weight of edges crossing the $(T,V\setminus T)$ cut in cost (resp. demand) graph and $\Phi^*$ is the sparsity of the optimal cut. In words, we show that the non-uniform sparsest cut problem is easy when the generalized spectrum grows moderately fast. To the best of our knowledge, there were no results based on higher order spectra for non-uniform sparsest cut prior to this work. #R##N#Even for uniform sparsest cut, the quantitative aspects of our result are somewhat stronger than previous methods. Similar results hold for other expansion measures like edge expansion, normalized cut, and conductance, with the $r$'th smallest eigenvalue of the normalized Laplacian playing the role of $\lambda_r$ in the latter two cases. #R##N#Our proof is based on an l1-embedding of vectors from a semi-definite program from the Lasserre hierarchy. The embedded vectors are then rounded to a cut using standard threshold rounding. We hope that the ideas connecting $\ell_1$-embeddings to Lasserre SDPs will find other applications. Another aspect of the analysis is the adaptation of the column selection paradigm from our earlier work on rounding Lasserre SDPs [GS11] to pick a set of edges rather than vertices. This feature is important in order to extend the algorithms to non-uniform sparsest cut.

Connected through hyperedge HE-1 (induced by source paper "matrix decompositions and sparse graph regularity"):
  with members:
    - "a new regularity lemma and faster approximation algorithms for low threshold rank graphs"
    - "faster sdp hierarchy solvers for local rounding algorithms"
    - "rounding semidefinite programming hierarchies via global correlation"

Connected through hyperedge HE-2 (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"
    - "improved cheeger s inequality analysis of spectral partitioning algorithms through higher order spectral gap"
    - "a prg for lipschitz functions of polynomials with applications to sparsest cut"

Connected through hyperedge HE-3 (induced by source paper "embedding approximately low dimensional ell_2 2 metrics into ell_1"):
  with members:
    - "guruswami sinop rounding without higher level lasserre"
    - "improved arv rounding in small set expanders and graphs of bounded threshold rank"
    - "improved cheeger s inequality analysis of spectral partitioning algorithms through higher order spectral gap"
    - "rounding semidefinite programming hierarchies via global correlation"
    - "a log n omega 1 integrality gap for the sparsest cut sdp"

Connected through hyperedge HE-4 (induced by source paper "guruswami sinop rounding without higher level lasserre"):
  with members:
    - "rounding lasserre sdps using column selection and spectrum based approximation schemes for graph partitioning and quadratic ips"
    - "towards a better approximation for sparsest cut"
    - "faster sdp hierarchy solvers for local rounding algorithms"
    - "optimal column based low rank matrix reconstruction"
    - "a log n omega 1 integrality gap for the sparsest cut sdp"

Connected through hyperedge HE-5 (induced by source paper "graph isomorphism and the lasserre hierarchy"):
  with members:
    - "hardness of robust graph isomorphism lasserre gaps and asymmetry of random graphs"
    - "analysis of boolean functions"
    - "practical graph isomorphism ii"

Connected through hyperedge HE-6 (induced by source paper "rounding lasserre sdps using column selection and spectrum based approximation schemes for graph partitioning and quadratic ips"):
  with members:
    - "faster sdp hierarchy solvers for local rounding algorithms"
    - "hypercontractivity sum of squares proofs and their applications"
    - "majority is stablest discrete and sos"
    - "approximability and proof complexity"
    - "rounding semidefinite programming hierarchies via global correlation"
    - "optimal column based low rank matrix reconstruction"
    - "spectral algorithms for unique games"
    - "near optimal column based matrix reconstruction"

Connected through hyperedge HE-7 (induced by source paper "improved cheeger s inequality analysis of spectral partitioning algorithms through higher order spectral gap"):
  with members:
    - "clustering is difficult only when it does not matter"
    - "on the practically interesting instances of maxcut"
    - "many sparse cuts via higher eigenvalues"
    - "faster sdp hierarchy solvers for local rounding algorithms"
    - "approximating the expansion profile and almost optimal local graph clustering"
    - "empirical comparison of algorithms for network community detection"
    - "are stable instances easy"
    - "eigenvalue bounds spectral partitioning and metrical deformations via flows"

Connected through hyperedge HE-8 (induced by source paper "a new regularity lemma and faster approximation algorithms for low threshold rank graphs"):
  with members:
    - "faster sdp hierarchy solvers for local rounding algorithms"
    - "rounding semidefinite programming hierarchies via global correlation"
    - "spectral algorithms for unique games"

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)