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: "distributed abstract optimization via constraints consensus theory and applications"
  Abstract: Distributed abstract programs are a novel class of distributed optimization problems where (i) the number of variables is much smaller than the number of constraints and (ii) each constraint is associated to a network node. Abstract optimization programs are a generalization of linear programs that captures numerous geometric optimization problems. We propose novel constraints consensus algorithms for distributed abstract programs: as each node iteratively identifies locally active constraints and exchanges them with its neighbors, the network computes the active constraints determining the global optimum. The proposed algorithms are appropriate for networks with weak time-dependent connectivity requirements and tight memory constraints. We show how suitable target localization and formation control problems can be tackled via constraints consensus.

Connected through hyperedge HE-1 (induced by source paper "social power evolution in influence networks with stubborn individuals"):
  with members:
    - "stability theory of stochastic models in opinion dynamics"
    - "a tutorial on modeling and analysis of dynamic social networks part i"
    - "novel multidimensional models of opinion dynamics in social networks"
    - "evolution of social power in social networks with dynamic topology"

Connected through hyperedge HE-2 (induced by source paper "distributed submodular minimization over networks a greedy column generation approach"):
  with members:
    - "distributed submodular maximization with limited information"
    - "reflection methods for user friendly submodular optimization"
    - "a supermodular optimization framework for leader selection under link noise in linear multi agent systems"
    - "learning with submodular functions a convex optimization perspective"
    - "efficient minimization of decomposable submodular functions"
    - "structured sparsity inducing norms through submodular functions"

Connected through hyperedge HE-3 (induced by source paper "a duality based approach for distributed min max optimization with application to demand side management"):
  with members:
    - "a polyhedral approximation framework for convex and robust distributed optimization"
    - "distributed constrained optimization by consensus based primal dual perturbation method"
    - "a regularized saddle point algorithm for networked optimization with resource allocation constraints"

Connected through hyperedge HE-4 (induced by source paper "distributed multi resource allocation with little communication overhead"):
  with members:
    - "distributed constrained optimization by consensus based primal dual perturbation method"
    - "fair assignment of indivisible objects under ordinal preferences"
    - "reaching an optimal consensus dynamical systems that compute intersections of convex sets"

Connected through hyperedge HE-5 (induced by source paper "asynchronous distributed optimization via randomized dual proximal gradient"):
  with members:
    - "a polyhedral approximation framework for convex and robust distributed optimization"
    - "parallel selective algorithms for big data optimization"
    - "convergence rates of distributed nesterov like gradient methods on random networks"
    - "explicit convergence rate of a distributed alternating direction method of multipliers"
    - "a fast distributed proximal gradient method"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"

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)