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: "networked parallel algorithms for robust convex optimization via the scenario approach"
  Abstract: This chapter proposes a parallel computing framework to distributedly solve robust convex optimization (RCO) when the constraints are affected by nonlinear uncertainty. To this end, we adopt a scenario approach by randomly sampling the uncertainty set. However, the number of samples to attain high levels of probabilistic guarantee of robustness may be large, which results in a large number of constraints in the scenario problem (SP). Instead of using a single processor, we resort to many processors that are distributed among different nodes of time-varying unbalanced digraphs. Then, we propose a random projected algorithm solve the SP, which is given in an explicitly recursive form with simple iteration. We show that if the sequence of digraphs are uniformly jointly strongly connected (UJSC), each node asymptotically converges to a common optimal solution to the SP. That is, the RCO is effectively solved in a distributed parallel way.

Connected through hyperedge HE-1 (induced by source paper "distributed random fixed projected algorithm for constrained optimization over digraphs"):
  with members:
    - "average consensus on arbitrary strongly connected digraphs with time varying topologies"
    - "average consensus on general strongly connected digraphs"

Connected through hyperedge HE-2 (induced by source paper "distributed convex optimization with inequality constraints over time varying unbalanced digraphs"):
  with members:
    - "distributed random fixed projected algorithm for constrained optimization over digraphs"
    - "distributed nonconvex multiagent optimization over time varying networks"
    - "explicit convergence rate of a distributed alternating direction method of multipliers"
    - "average consensus on general strongly connected digraphs"

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.DC(Distributed, Parallel, and Cluster Computing)