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: "flexible parallel algorithms for big data optimization"
  Abstract: We propose a decomposition framework for the parallel optimization of the sum of a differentiable function and a (block) separable nonsmooth, convex one. The latter term is typically used to enforce structure in the solution as, for example, in Lasso problems. Our framework is very flexible and includes both fully parallel Jacobi schemes and Gauss-Seidel (Southwell-type) ones, as well as virtually all possibilities in between (e.g., gradient- or Newton-type methods) with only a subset of variables updated at each iteration. Our theoretical convergence results improve on existing ones, and numerical results show that the new method compares favorably to existing algorithms.

Connected through hyperedge HE-1 (induced by source paper "parallel stochastic optimization framework for large scale non convex stochastic problems"):
  with members:
    - "an energy efficient controller for wirelessly powered communication networks"
    - "efficient fair and qos aware policies for wirelessly powered communication networks"
    - "accelerating minibatch stochastic gradient descent using typicality sampling"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "on breast cancer detection an application of machine learning algorithms on the wisconsin diagnostic dataset"
    - "energy entropy competition and the effectiveness of stochastic gradient descent in machine learning"
    - "optimal hierarchical radio resource management for hetnets with flexible backhaul"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "success probability and area spectral efficiency in multiuser mimo hetnets"):
  with members:
    - "large scale mimo versus network mimo for multicell interference mitigation"
    - "downlink cellular network analysis with multi slope path loss models"
    - "user centric intercell interference nulling for downlink small cell networks"
    - "what will 5g be"
    - "massive mimo meets hetnet interference coordination through spatial blanking"
    - "throughput and energy efficiency analysis of small cell networks with multi antenna base stations"
    - "downlink mimo hetnets modeling ordering results and performance analysis"
    - "scaling up mimo opportunities and challenges with very large arrays"

Connected through hyperedge HE-3 (induced by source paper "a distributed block coordinate descent method for training l_1 regularized linear classifiers"):
  with members:
    - "distributed soft thresholding for sparse signal recovery"
    - "parallel coordinate descent newton method for efficient ell_1 regularized minimization"
    - "a reliable effective terascale linear learning system"
    - "parallel coordinate descent for l1 regularized loss minimization"

Connected through hyperedge HE-4 (induced by source paper "hybrid random deterministic parallel algorithms for nonconvex big data optimization"):
  with members:
    - "decomposition by partial linearization parallel optimization of multi agent systems"
    - "optimization with sparsity inducing penalties"
    - "parallel coordinate descent for l1 regularized loss minimization"

Connected through hyperedge HE-5 (induced by source paper "parallel selective algorithms for big data optimization"):
  with members:
    - "decomposition by partial linearization parallel optimization of multi agent systems"
    - "optimization with sparsity inducing penalties"
    - "parallel coordinate descent for l1 regularized loss minimization"

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)