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 approximability of minmax regret network optimization problems"
  Abstract: In this paper the minmax (regret) versions of some basic polynomially solvable deterministic network problems are discussed. It is shown that if the number of scenarios is unbounded, then the problems under consideration are not approximable within $\log^{1-\epsilon} K$ for any $\epsilon>0$ unless NP $\subseteq$ DTIME$(n^{\mathrm{poly} \log n})$, where $K$ is the number of scenarios.

Connected through hyperedge HE-1 (induced by source paper "robust submodular minimization with applications to cooperative modeling"):
  with members:
    - "near optimal algorithms for hard submodular programs with discounted cooperative costs"
    - "approximating some network problems with scenarios"
    - "robust optimization for non convex objectives"
    - "robust submodular maximization offline and online algorithms"
    - "equilibrium computation and robust optimization in zero sum games with submodular structure"
    - "polyhedral aspects of submodularity convexity and concavity"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "curvature and optimal algorithms for learning and minimizing submodular functions"

Connected through hyperedge HE-2 (induced by source paper "a unified framework of robust submodular optimization"):
  with members:
    - "near optimal algorithms for hard submodular programs with discounted cooperative costs"
    - "learning from less data a unified data subset selection and active learning framework for computer vision"
    - "robust monotone submodular function maximization"
    - "distributionally robust submodular maximization"
    - "approximating some network problems with scenarios"
    - "a framework towards domain specific video summarization"
    - "robust optimization for non convex objectives"
    - "robust submodular maximization offline and online algorithms"

Connected through hyperedge HE-3 (induced by source paper "robust two stage combinatorial optimization problems under convex uncertainty"):
  with members:
    - "two stage combinatorial optimization problems under risk"
    - "robust recoverable and two stage selection problems"
    - "approximability of the robust representatives selection problem"
    - "robust optimization with incremental recourse"

Connected through hyperedge HE-4 (induced by source paper "robust recoverable and two stage selection problems"):
  with members:
    - "recoverable robust spanning tree problem under interval uncertainty representations"
    - "improved approximation algorithms for the min max selecting items problem"
    - "robust optimization with incremental recourse"
    - "analytical approach to parallel repetition"

Connected through hyperedge HE-5 (induced by source paper "using the wowa operator in robust discrete optimization problems"):
  with members:
    - "combinatorial optimization problems with uncertain costs and the owa criterion"
    - "exact algorithms for owa optimization in multiobjective spanning tree problems"

Connected through hyperedge HE-6 (induced by source paper "approximability of the robust representatives selection problem"):
  with members:
    - "improved approximation algorithms for the min max selecting items problem"

Connected through hyperedge HE-7 (induced by source paper "combinatorial optimization problems with uncertain costs and the owa criterion"):
  with members:
    - "improved approximation algorithms for the min max selecting items problem"
    - "exact algorithms for owa optimization in multiobjective spanning tree 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.CC(Computational Complexity)