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: "on the shadow simplex method for curved polyhedra"
  Abstract: We study the simplex method over polyhedra satisfying certain "discrete curvature" lower bounds, which enforce that the boundary always meets vertices at sharp angles. Motivated by linear programs with totally unimodular constraint matrices, recent results of Bonifas et al (SOCG 2012), Brunsch and R\"oglin (ICALP 2013), and Eisenbrand and Vempala (2014) have improved our understanding of such polyhedra. #R##N#We develop a new type of dual analysis of the shadow simplex method which provides a clean and powerful tool for improving all previously mentioned results. Our methods are inspired by the recent work of Bonifas and the first named author (SODA 2015), who analyzed a remarkably similar process as part of an algorithm for the Closest Vector Problem with Preprocessing. #R##N#For our first result, we obtain a constructive diameter bound of $O(\frac{n^2}{\delta} \ln \frac{n}{\delta})$ for $n$-dimensional polyhedra with curvature parameter $\delta \in [0,1]$. For the class of polyhedra arising from totally unimodular constraint matrices, this implies a bound of $O(n^3 \ln n)$. For linear optimization, given an initial feasible vertex, we show that an optimal vertex can be found using an expected $O(\frac{n^3}{\delta} \ln \frac{n}{\delta})$ simplex pivots, each requiring $O(m n)$ time to compute. An initial feasible solution can be found using $O(\frac{m n^3}{\delta} \ln \frac{n}{\delta})$ pivot steps.

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"
    - "geometric random edge"
    - "solving totally unimodular lps with the shadow vertex algorithm"
    - "the simplex method is strongly polynomial for deterministic markov decision processes"
    - "beyond hirsch conjecture walks on random polytopes and smoothed complexity of the simplex method"
    - "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 "geometric random edge"):
  with members:
    - "solving totally unimodular lps with the shadow vertex algorithm"
    - "a cubic algorithm for computing gaussian volume"
    - "finding short paths on polytopes by the shadow vertex algorithm"
    - "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)