human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "communication efficient asynchronous stochastic frank wolfe over nuclear norm balls"):
  Abstract: Large-scale machine learning training suffers from two prior challenges, specifically for nuclear-norm constrained problems with distributed systems: the synchronization slowdown due to the straggling workers, and high communication costs. In this work, we propose an asynchronous Stochastic Frank Wolfe (SFW-asyn) method, which, for the first time, solves the two problems simultaneously, while successfully maintaining the same convergence rate as the vanilla SFW. We implement our algorithm in python (with MPI) to run on Amazon EC2, and demonstrate that SFW-asyn yields speed-ups almost linear to the number of machines compared to the vanilla SFW.

Includes members:
  Member "quantized frank wolfe communication efficient distributed optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "coke communication censored kernel learning for decentralized non parametric learning")
  Member "quantized frank wolfe faster optimization lower communication and projection free":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient projection free online methods with stochastic recursive gradient")
  Member "a distributed frank wolfe framework for learning low rank matrices with the trace norm":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "quantized frank wolfe faster optimization lower communication and projection free")
  Member "algorithmic regularization in over parameterized matrix recovery":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "revisiting landscape analysis in deep neural networks eliminating decreasing paths to infinity")
  Member "algorithmic regularization in over parameterized matrix sensing and neural networks with quadratic activations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the implicit and explicit regularization effects of dropout")
  Member "linear convergence of a frank wolfe type algorithm over trace norm balls":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "faster projection free online learning")
  Member "variance reduced and projection free stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the convergence of stochastic gradient descent with low rank projections for convex low rank matrix problems")
  Member "passcode parallel asynchronous stochastic dual co ordinate descent":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "async a cloud engine with asynchrony and history for distributed machine learning")

Question: Please tell me which class the source paper 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)