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: "eigenvalue bounds spectral partitioning and metrical deformations via flows"
  Abstract: We present a new method for upper bounding the second eigenvalue of the Laplacian of graphs. Our approach uses multi-commodity flows to deform the geometry of the graph; we embed the resulting metric into Euclidean space to recover a bound on the Rayleigh quotient. Using this, we show that every $n$-vertex graph of genus $g$ and maximum degree $d$ satisfies $\lambda_2(G) = O((g+1)^3 d/n)$. This recovers the $O(d/n)$ bound of Spielman and Teng for planar graphs, and compares to Kelner's bound of $O((g+1) poly(d)/n)$, but our proof does not make use of conformal mappings or circle packings. We are thus able to extend this to resolve positively a conjecture of Spielman and Teng, by proving that $\lambda_2(G) = O(d h^6 \log h/n)$ whenever $G$ is $K_h$-minor free. This shows, in particular, that spectral partitioning can be used to recover $O(\sqrt{n})$-sized separators in bounded degree graphs that exclude a fixed minor. We extend this further by obtaining nearly optimal bounds on $\lambda_2$ for graphs which exclude small-depth minors in the sense of Plotkin, Rao, and Smith. Consequently, we show that spectral algorithms find small separators in a general class of geometric graphs. #R##N#Moreover, while the standard "sweep" algorithm applied to the second eigenvector may fail to find good quotient cuts in graphs of unbounded degree, our approach produces a vector that works for arbitrary graphs. This yields an alternate proof of the result of Alon, Seymour, and Thomas that every excluded-minor family of graphs has $O(\sqrt{n})$-node balanced separators.

Connected through hyperedge HE-1 (induced by source paper "a ptas for subset tsp in minor free graphs"):
  with members:
    - "truly optimal euclidean spanners"
    - "greedy spanners are optimal in doubling metrics"
    - "on subexponential parameterized algorithms for steiner tree and directed subset tsp on planar graphs"
    - "minor free graphs have light spanners"
    - "the greedy spanner is existentially optimal"
    - "linear size distance preservers"
    - "graph minors for preserving terminal distances approximately lower and upper bounds"
    - "a light metric spanner"

Connected through hyperedge HE-2 (induced by source paper "local search is a ptas for feedback vertex set in minor free graphs"):
  with members:
    - "local search yields approximation schemes for k means and k median in euclidean and minor free metrics"
    - "approximation algorithms for polynomial expansion and low density graphs"
    - "simple ptas s for families of graphs excluding a minor"
    - "solving the undirected feedback vertex set problem by local search"
    - "approximation algorithms for maximum independent set of pseudo disks"
    - "separator theorems for minor free and shallow minor free graphs with applications"
    - "bidimensionality and eptas"

Connected through hyperedge HE-3 (induced by source paper "spectral concentration and greedy k clustering"):
  with members:
    - "how to round subspaces a new spectral clustering algorithm"
    - "testing cluster structure of graphs"
    - "the hardness of approximation of euclidean k means"
    - "partitioning well clustered graphs spectral clustering works"
    - "flow based algorithms for local graph clustering"
    - "partitioning into expanders"
    - "improved cheeger s inequality analysis of spectral partitioning algorithms through higher order spectral gap"
    - "many sparse cuts via higher eigenvalues"

Connected through hyperedge HE-4 (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"
    - "approximating non uniform sparsest cut via generalized spectra"
    - "empirical comparison of algorithms for network community detection"
    - "are stable instances easy"

Connected through hyperedge HE-5 (induced by source paper "cops robbers and threatening skeletons padded decomposition for minor free graphs"):
  with members:
    - "separator theorems for minor free and shallow minor free graphs with applications"
    - "randomly removing g handles at once"
    - "optimal stochastic planarization"

Connected through hyperedge HE-6 (induced by source paper "structured recursive separator decompositions for planar graphs in linear time"):
  with members:
    - "separator theorems for minor free and shallow minor free graphs with applications"
    - "linear space approximate distance oracles for planar bounded genus and minor free graphs"
    - "exact distance oracles for planar graphs"
    - "shortest paths in planar graphs with real lengths in o n log 2n log log n time"
    - "maintaining information in fully dynamic trees with top trees"
    - "dynamic generators of topologically embedded graphs"

Connected through hyperedge HE-7 (induced by source paper "many sparse cuts via higher eigenvalues"):
  with members:
    - "reductions between expansion problems"

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)