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: "many sparse cuts via higher eigenvalues"
  Abstract: Cheeger's fundamental inequality states that any edge-weighted graph has a vertex subset S such that its expansion (a.k.a. conductance) is bounded as follows: [ φ(S) def= (w(S,bar{S}))/(min set(w(S), w(bar(S)))) ≤ √(2 λ2) ] where w is the total edge weight of a subset or a cut and λ2 is the second smallest eigenvalue of the normalized Laplacian of the graph. Here we prove the following natural generalization: for any integer k ∈ [n], there exist ck disjoint subsets S1, ..., Sck, such that [ maxi φ(Si) ≤ C √(λk log k) ] where λk is the kth smallest eigenvalue of the normalized Laplacian and c 0 are suitable absolute constants. Our proof is via a polynomial-time algorithm to find such subsets, consisting of a spectral projection and a randomized rounding. As a consequence, we get the same upper bound for the small set expansion problem, namely for any k, there is a subset S whose weight is at most a O(1/k) fraction of the total weight and φ(S) ≤ C √(λk log k). Both results are the best possible up to constant factors.     The underlying algorithmic problem, namely finding k subsets such that the maximum expansion is minimized, besides extending sparse cuts to more than one subset, appears to be a natural clustering problem in its own right.

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"
    - "approximation algorithm for sparsest k partitioning"
    - "improved cheeger s inequality analysis of spectral partitioning algorithms through higher order spectral gap"

Connected through hyperedge HE-3 (induced by source paper "spectral algorithms for temporal graph cuts"):
  with members:
    - "geometric deep learning going beyond euclidean data"
    - "an introduction to temporal graphs an algorithmic perspective"
    - "on temporal graph exploration"
    - "active semi supervised learning using sampling theory for graph signals"
    - "a generalized cheeger inequality"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "is cheeger type approximation possible for nonuniform sparsest cut"
    - "a survey on multi view learning"

Connected through hyperedge HE-4 (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"
    - "improved cheeger s inequality analysis of spectral partitioning algorithms through higher order spectral gap"
    - "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-5 (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"
    - "improved cheeger s inequality analysis of spectral partitioning algorithms through higher order spectral gap"
    - "a quasi polynomial time partition oracle for graphs with an excluded minor"

Connected through hyperedge HE-6 (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"
    - "approximation algorithm for sparsest k partitioning"
    - "improved cheeger s inequality analysis of spectral partitioning algorithms through higher order spectral gap"
    - "reductions between expansion problems"
    - "most tensor problems are np hard"

Connected through hyperedge HE-7 (induced by source paper "testing cluster structure of graphs"):
  with members:
    - "partitioning well clustered graphs spectral clustering works"
    - "partitioning well clustered graphs with k means and heat kernel"
    - "spectral concentration and greedy k clustering"
    - "flow based algorithms for local graph clustering"
    - "partitioning into expanders"
    - "optimal algorithms for testing closeness of discrete distributions"
    - "approximating the expansion profile and almost optimal local graph clustering"
    - "testing closeness of discrete distributions"

Connected through hyperedge HE-8 (induced by source paper "how to round subspaces a new spectral clustering algorithm"):
  with members:
    - "spectral clustering via the power method provably"
    - "approximation algorithm for sparsest k partitioning"
    - "improved cheeger s inequality analysis of spectral partitioning algorithms through higher order spectral gap"
    - "improved spectral norm bounds for clustering"
    - "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"

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)