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: "local max cut in smoothed polynomial time"
  Abstract: In 1988, Johnson, Papadimitriou and Yannakakis wrote that "Practically all the empirical evidence would lead us to conclude that finding locally optimal solutions is much easier than solving NP-hard problems". Since then the empirical evidence has continued to amass, but formal proofs of this phenomenon have remained elusive. A canonical (and indeed complete) example is the local max-cut problem, for which no polynomial time method is known. In a breakthrough paper, Etscheid and Roglin proved that the smoothed complexity of local max-cut is quasi-polynomial, i.e., if arbitrary bounded weights are randomly perturbed, a local maximum can be found in $n^{O(\log n)}$ steps. In this paper we prove smoothed polynomial complexity for local max-cut, thus confirming that finding local optima for max-cut is much easier than solving it.

Connected through hyperedge HE-1 (induced by source paper "node max cut and computing equilibria in linear weighted congestion games"):
  with members:
    - "computing approximate pure nash equilibria in shapley value weighted congestion games"
    - "efficient computation of approximate pure nash equilibria in congestion games"
    - "approximate pure nash equilibria in weighted congestion games existence efficient computation and structure"

Connected through hyperedge HE-2 (induced by source paper "smoothed complexity of local max cut and binary max csp"):
  with members:
    - "smoothed efficient algorithms and reductions for network coordination games"
    - "settling the complexity of local max cut almost completely"
    - "smoothed analysis of algorithms why the simplex algorithm usually takes polynomial time"

Connected through hyperedge HE-3 (induced by source paper "beyond worst case analysis"):
  with members:
    - "being robust in high dimensions can be practical"
    - "implicit regularization in deep learning"
    - "a friendly smoothed analysis of the simplex method"
    - "how to escape saddle points efficiently"
    - "a nearly tight sum of squares lower bound for the planted clique problem"
    - "understanding deep learning requires rethinking generalization"
    - "how robust are reconstruction thresholds for community detection"
    - "k center clustering under perturbation resilience"

Connected through hyperedge HE-4 (induced by source paper "smoothed analysis in unsupervised learning via decoupling"):
  with members:
    - "smoothed analysis of discrete tensor decomposition and assemblies of neurons"
    - "learning overcomplete hmms"
    - "polynomial time tensor decompositions with sum of squares"
    - "learning mixtures of gaussians in high dimensions"
    - "fast spectral algorithms from sum of squares proofs tensor decomposition and planted sparse vectors"
    - "minimal realization problems for hidden markov models"
    - "dictionary learning and tensor decomposition via the sum of squares method"
    - "smoothed analysis of tensor decompositions"

Connected through hyperedge HE-5 (induced by source paper "ppp completeness with connections to cryptography"):
  with members:
    - "on the polynomial parity argument complexity of the combinatorial nullstellensatz"
    - "consensus halving is ppa complete"
    - "cls new problems and completeness"
    - "a converse to banach s fixed point theorem and its cls completeness"
    - "integer factoring and modular square roots"
    - "settling the complexity of computing approximate two player nash equilibria"
    - "number balancing is as hard as minkowski s theorem and shortest vector"
    - "on the complexity of nash equilibria in anonymous games"

Connected through hyperedge HE-6 (induced by source paper "nash equilibrium in smoothed polynomial time for network coordination games"):
  with members:
    - "near optimal communication lower bounds for approximate nash equilibria"
    - "an empirical study on computing equilibria in polymatrix games"
    - "communication complexity of approximate nash equilibria"
    - "inapproximability of nash equilibrium"
    - "constant rank bimatrix games are ppad hard"
    - "query complexity of approximate nash equilibria"
    - "bilinear games polynomial time algorithms for rank based subclasses"
    - "an experimental analysis of lemke howson algorithm"

Connected through hyperedge HE-7 (induced by source paper "a converse to banach s fixed point theorem and its cls completeness"):
  with members:
    - "cls new problems and completeness"
    - "settling the complexity of computing approximate two player nash equilibria"
    - "understanding alternating minimization for matrix completion"
    - "settling the complexity of computing two player nash equilibria"

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)