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: "average case polyhedral complexity of the maximum stable set problem"
  Abstract: We study the minimum number of constraints needed to formulate random instances of the maximum stable set problem via linear programs (LPs), in two distinct models. In the uniform model, the constraints of the LP are not allowed to depend on the input graph, which should be encoded solely in the objective function. There we prove a $$2^{\Omega (n{/}\log n)}$$2Ω(n/logn) lower bound with probability at least $$1 - 2^{-2^n}$$1-2-2n for every LP that is exact for a randomly selected set of instances; each graph on at most n vertices being selected independently with probability $$p \geqslant 2^{- \left( {\begin{array}{c}n/4\\ 2\end{array}}\right) + n}$$pź2-n/42+n. In the non-uniform model, the constraints of the LP may depend on the input graph, but we allow weights on the vertices. The input graph is sampled according to the G(n, p) model. There we obtain upper and lower bounds holding with high probability for various ranges of p. We obtain a super-polynomial lower bound all the way from $$p = \Omega \left( \frac{\log ^{6+\varepsilon } n}{n} \right) $$p=Ωlog6+źnn to $$p = o\left( \frac{1}{\log n} \right) $$p=o1logn. Our upper bound is close to this as there is only an essentially quadratic gap in the exponent, which currently also exists in the worst-case model. Finally, we state a conjecture that would close this gap, both in the average-case and worst-case models.

Connected through hyperedge HE-1 (induced by source paper "nondeterministic communication complexity of random boolean functions"):
  with members:
    - "communication complexity for algorithm designers"
    - "independent sets in random graphs from the weighted second moment method"

Connected through hyperedge HE-2 (induced by source paper "the matching polytope does not admit fully polynomial size relaxation schemes"):
  with members:
    - "affine reductions for lps and sdps"
    - "lower bounds on the size of semidefinite programming relaxations"
    - "exponential lower bounds on the size of approximate formulations in the natural encoding for capacitated facility location"
    - "approximate constraint satisfaction requires large lp relaxations"
    - "on the existence of 0 1 polytopes with high semidefinite extension complexity"
    - "the matching polytope has exponential extension complexity"
    - "approximation limits of linear programs beyond hierarchies"
    - "extended formulations for polygons"

Connected through hyperedge HE-3 (induced by source paper "affine reductions for lps and sdps"):
  with members:
    - "extension complexity of independent set polytopes"
    - "strong reductions for extended formulations"
    - "approximating rectangles by juntas and weakly exponential lower bounds for lp relaxations of csps"
    - "the matching polytope does not admit fully polynomial size relaxation schemes"
    - "the matching problem has no small symmetric sdp"
    - "no small linear program approximates vertex cover within a factor 2 epsilon"
    - "lower bounds on the size of semidefinite programming relaxations"
    - "approximate constraint satisfaction requires large lp relaxations"

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.CC(Computational Complexity)