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 strongly convex optimization"
  Abstract: A lot of effort has been invested into characterizing the convergence rates of gradient based algorithms for non-linear convex optimization. Recently, motivated by large datasets and problems in machine learning, the interest has shifted towards distributed optimization. In this work we present a distributed algorithm for strongly convex constrained optimization. Each node in a network of n computers converges to the optimum of a strongly convex, L-Lipchitz continuous, separable objective at a rate O(log (sqrt(n) T) / T) where T is the number of iterations. This rate is achieved in the online setting where the data is revealed one at a time to the nodes, and in the batch setting where each node has access to its full local dataset from the start. The same convergence rate is achieved in expectation when the subgradients used at each node are corrupted with additive zero-mean noise.

Connected through hyperedge HE-1 (induced by source paper "clustering with distributed data"):
  with members:
    - "communication efficient learning of deep networks from decentralized data"
    - "distributed nonconvex multiagent optimization over time varying networks"
    - "adding vs averaging in distributed primal dual optimization"
    - "distributed k means algorithm"
    - "distributed k means and k median clustering on general topologies"
    - "scalable k means"
    - "fast distributed gradient methods"
    - "gossip algorithms for distributed signal processing"

Connected through hyperedge HE-2 (induced by source paper "proximity without consensus in online multiagent optimization"):
  with members:
    - "multitask diffusion adaptation over networks"
    - "on the learning behavior of adaptive networks part i transient analysis"
    - "fast distributed gradient methods"
    - "trading regret for efficiency online convex optimization with long term constraints"

Connected through hyperedge HE-3 (induced by source paper "network flows that solve linear equations"):
  with members:
    - "decentralized gradient algorithm for solution of a linear equation"
    - "a distributed algorithm for solving a linear algebraic equation"
    - "a fast distributed solver for symmetric diagonally dominant linear equations"
    - "solving systems of linear equations locally and asynchronously"
    - "fast distributed gradient methods"
    - "cooperative convex optimization in networked systems augmented lagrangian algorithms with directed gossip communication"
    - "randomized optimal consensus of multi agent systems"
    - "reaching an optimal consensus dynamical systems that compute intersections of convex sets"

Connected through hyperedge HE-4 (induced by source paper "a distributed algorithm for solving a linear algebraic equation"):
  with members:
    - "distributed constrained optimization by consensus based primal dual perturbation method"
    - "a fast distributed proximal gradient method"
    - "fast distributed gradient methods"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"

Connected through hyperedge HE-5 (induced by source paper "information exchange and learning dynamics over weakly connected adaptive networks"):
  with members:
    - "distributed detection and estimation in wireless sensor networks"
    - "multitask diffusion adaptation over networks"
    - "distributed pareto optimization via diffusion strategies"
    - "on the learning behavior of adaptive networks part ii performance analysis"
    - "on the learning behavior of adaptive networks part i transient analysis"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"
    - "higher dimensional consensus learning in large scale networks"
    - "gossip algorithms for distributed signal processing"

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)