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: "a unified view of regularized dual averaging and mirror descent with implicit updates"
  Abstract: We study three families of online convex optimization algorithms: follow-the-proximally-regularized-leader (FTRL-Proximal), regularized dual averaging (RDA), and composite-objective mirror descent. We first prove equivalence theorems that show all of these algorithms are instantiations of a general FTRL update. This provides theoretical insight on previous experimental observations. In particular, even though the FOBOS composite mirror descent algorithm handles L1 regularization explicitly, it has been observed that RDA is even more effective at producing sparsity. Our results demonstrate that FOBOS uses subgradient approximations to the L1 penalty from previous rounds, leading to less sparsity than RDA, which handles the cumulative penalty in closed form. The FTRL-Proximal algorithm can be seen as a hybrid of these two, and outperforms both on a large, real-world dataset. #R##N#Our second contribution is a unified analysis which produces regret bounds that match (up to logarithmic terms) or improve the best previously known bounds. This analysis also extends these algorithms in two important ways: we support a more general type of composite objective and we analyze implicit updates, which replace the subgradient approximation of the current loss function with an exact optimization.

Connected through hyperedge HE-1 (induced by source paper "user preference learning based edge caching for fog radio access network"):
  with members:
    - "distributed edge caching in ultra dense fog radio access networks a mean field approach"
    - "context aware proactive content caching with service differentiation in wireless networks"
    - "a learning based approach to caching in heterogenous small cell networks"
    - "fog computing based radio access networks issues and challenges"
    - "big data caching for networking moving from cloud to edge"
    - "cooperative caching and transmission design in cluster centric small cell networks"
    - "a transfer learning approach for cache enabled wireless networks"
    - "system architecture and key technologies for 5g heterogeneous cloud radio access networks"

Connected through hyperedge HE-2 (induced by source paper "fully implicit online learning"):
  with members:
    - "introduction to online convex optimization"
    - "online bayesian passive aggressive learning"
    - "exact soft confidence weighted learning"

Connected through hyperedge HE-3 (induced by source paper "linear coupling an ultimate unification of gradient and mirror descent"):
  with members:
    - "using optimization to break the epsilon barrier a faster and simpler width independent algorithm for solving positive linear programs in parallel"
    - "an almost linear time algorithm for approximate max flow in undirected graphs and its multicommodity generalizations"
    - "efficient accelerated coordinate descent methods and faster algorithms for solving linear systems"
    - "navigating central path with electrical flows from flows to matchings and back"
    - "nearly maximum flows in nearly linear time"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"
    - "adaptive bound optimization for online convex optimization"
    - "optimal distributed online prediction using mini batches"

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.LG(Machine Learning)