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 "quantized frank wolfe faster optimization lower communication and projection free"):
  Abstract: How can we efficiently mitigate the overhead of gradient communications in distributed optimization? This problem is at the heart of training scalable machine learning models and has been mainly studied in the unconstrained setting. In this paper, we propose Quantized-Frank-Wolfe (QFW), the first projection-free and communication-efficient algorithm for solving constrained optimization problems at scale. We consider both convex and non-convex objective functions, expressed as a finite-sum or more generally a stochastic optimization problem, and provide strong theoretical guarantees on the convergence rate of QFW. This is accomplished by proposing novel quantization schemes that efficiently compress gradients while controlling the noise variance introduced during this process. Finally, we empirically validate the efficiency of QFW in terms of communication and the quality of returned solution against natural baselines.

Includes members:
  Member "constrained deep learning using conditional gradient and applications in computer vision":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the empirical duality gap of constrained statistical learning")
  Member "deep frank wolfe for neural network optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "safe screening for the generalized conditional gradient method")
  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 "communication efficient asynchronous stochastic frank wolfe over nuclear norm balls")
  Member "neural conditional gradients":
    also connected through other hyperedges:
      (none)
  Member "sparsified sgd with memory":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sparse weight activation training")
  Member "signsgd compressed optimisation for non convex problems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "communication optimization strategies for distributed deep learning a survey")
  Member "learning infinite rbms with frank wolfe":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on better training the infinite restricted boltzmann machines")
  Member "terngrad ternary gradients to reduce communication in distributed deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "communication optimization strategies for distributed deep learning a survey")

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)