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 "an adaptive remote stochastic gradient method for training neural networks"):
  Abstract: We introduce ARSG, an adaptive first-order algorithm for training neural networks. The method computes the gradients at configurable remote observation points, in order to expedite the convergence by adjusting the step size for directions with different curvatures in the stochastic setting. It also scales the updating increment elementwise by a nonincreasing preconditioner to incorporate the avdantage of adaptive methods. The method is efficient in computation and memory, and is straightforward to implement. We analyze the convergence properties for both convex and nonconvex problems by modeling the training process as a dynamic system, that provides a guideline to select the configurable observation factor without grid search. A data-dependent regret bound is proposed to guarantee the convergence in the convex setting, which can be further improved to $O(log(T))$ for strongly convex functions. Numerical experiments demonstrate that ARSG converges faster than popular adaptive methods, such as ADAM, NADAM, AMSGRAD, and RANGER for the tested problems.

Includes members:
  Member "on the variance of the adaptive learning rate and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "on the convergence of adam and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "disentangling adaptive gradient methods from learning rates")
  Member "sadam a variant of adam for strongly convex functions":
    also connected through other hyperedges:
      (none)
  Member "on the insufficiency of existing momentum schemes for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the two regimes of deep network training")
  Member "scaling sgd batch size to 32k for imagenet training":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "communication optimization strategies for distributed deep learning a survey")
  Member "accurate large minibatch sgd training imagenet in 1 hour":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning representations by predicting bags of visual words")
  Member "training neural networks without gradients a scalable admm approach":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep connections between learning from limited labels physical parameter estimation inspiration for regularization")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")

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)