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: "beyond hirsch conjecture walks on random polytopes and smoothed complexity of the simplex method"
  Abstract: The smoothed analysis of algorithms is concerned with the expected running time of an algorithm under slight random perturbations of arbitrary inputs. Spielman and Teng proved that the shadow-vertex simplex method has polynomial smoothed complexity. On a slight random perturbation of an arbitrary linear program, the simplex method finds the solution after a walk on polytope(s) with expected length polynomial in the number of constraints n, the number of variables d and the inverse standard deviation of the perturbation 1/sigma. #R##N#We show that the length of walk in the simplex method is actually polylogarithmic in the number of constraints n. Spielman-Teng's bound on the walk was O(n^{86} d^{55} sigma^{-30}), up to logarithmic factors. We improve this to O(log^7 n (d^9 + d^3 \s^{-4})). This shows that the tight Hirsch conjecture n-d on the length of walk on polytopes is not a limitation for the smoothed Linear Programming. Random perturbations create short paths between vertices. #R##N#We propose a randomized phase-I for solving arbitrary linear programs, which is of independent interest. Instead of finding a vertex of a feasible set, we add a vertex at random to the feasible set. This does not affect the solution of the linear program with constant probability. This overcomes one of the major difficulties of smoothed analysis of the simplex method -- one can now statistically decouple the walk from the smoothed linear program. This yields a much better reduction of the smoothed complexity to a geometric quantity -- the size of planar sections of random polytopes. We also improve upon the known estimates for that size, showing that it is polylogarithmic in the number of vertices.

Connected through hyperedge HE-1 (induced by source paper "a friendly smoothed analysis of the simplex method"):
  with members:
    - "smoothed analysis of the successive shortest path algorithm"
    - "on the shadow simplex method for curved polyhedra"
    - "geometric random edge"
    - "solving totally unimodular lps with the shadow vertex algorithm"
    - "the simplex method is strongly polynomial for deterministic markov decision processes"
    - "smoothed analysis of the condition numbers and growth factors of matrices"
    - "smoothed analysis of algorithms why the simplex algorithm usually takes polynomial time"

Connected through hyperedge HE-2 (induced by source paper "on the shadow simplex method for curved polyhedra"):
  with members:
    - "geometric random edge"
    - "short paths on the voronoi graph and the closest vector problem with preprocessing"
    - "finding short paths on polytopes by the shadow vertex algorithm"
    - "smoothed analysis of algorithms why the simplex algorithm usually takes polynomial time"

Connected through hyperedge HE-3 (induced by source paper "smoothed complexity theory"):
  with members:
    - "smoothed analysis of belief propagation for minimum cost flow and matching"
    - "improved smoothed analysis of multiobjective optimization"
    - "pareto optimal solutions for smoothed analysts"
    - "on smoothed analysis of quicksort and hoare s find"
    - "settling the complexity of computing two player nash equilibria"
    - "average case complexity"
    - "smoothed analysis of the condition numbers and growth factors of matrices"
    - "smoothed analysis of algorithms why the simplex algorithm usually takes polynomial time"

Connected through hyperedge HE-4 (induced by source paper "smoothed analysis of balancing networks"):
  with members:
    - "smoothed analysis of algorithms why the simplex algorithm usually takes polynomial time"

Connected through hyperedge HE-5 (induced by source paper "k means has polynomial smoothed complexity"):
  with members:
    - "improved smoothed analysis of the k means method"
    - "k means requires exponentially many iterations even in the plane"
    - "smoothed analysis of algorithms why the simplex algorithm usually takes polynomial time"

Connected through hyperedge HE-6 (induced by source paper "are stable instances easy"):
  with members:
    - "smoothed analysis of algorithms why the simplex algorithm usually takes polynomial time"

Connected through hyperedge HE-7 (induced by source paper "some problems in asymptotic convex geometry and random matrices motivated by numerical algorithms"):
  with members:
    - "smoothed analysis of the condition numbers and growth factors of matrices"
    - "smoothed analysis of algorithms why the simplex algorithm usually takes polynomial time"

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)