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: "variants of rmsprop and adagrad with logarithmic regret bounds"
  Abstract: Adaptive gradient methods have become recently very popular, in particular as they have been shown to be useful in the training of deep neural networks. In this paper we have analyzed RMSProp, originally proposed for the training of deep neural networks, in the context of online convex optimization and show $\sqrt{T}$-type regret bounds. Moreover, we propose two variants SC-Adagrad and SC-RMSProp for which we show logarithmic regret bounds for strongly convex functions. Finally, we demonstrate in the experiments that these new variants outperform other adaptive gradient techniques or stochastic gradient descent in the optimization of strongly convex functions as well as in training of deep neural networks.

Connected through hyperedge HE-1 (induced by source paper "compositional adam an adaptive compositional solver"):
  with members:
    - "online meta learning"
    - "a survey of optimization methods from a machine learning perspective"
    - "on the convergence of adam and beyond"
    - "derivative free order robust optimisation"
    - "toward multimodal model agnostic meta learning"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "bayesian model agnostic meta learning"
    - "learning to communicate implicitly by actions"

Connected through hyperedge HE-2 (induced by source paper "training neural networks for and by interpolation"):
  with members:
    - "sgd converges to global minimum in deep learning via star convex path"
    - "painless stochastic gradient interpolation line search and convergence rates"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "deepobs a deep learning optimizer benchmark suite"
    - "on the convergence of adam and beyond"
    - "l4 practical loss based stepsize adaptation for deep learning"
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"
    - "deep frank wolfe for neural network optimization"

Connected through hyperedge HE-3 (induced by source paper "reinforcement learning based vehicle cell association algorithm for highly mobile millimeter wave communication"):
  with members:
    - "vehicular edge computing via deep reinforcement learning"
    - "wireless edge computing with latency and reliability guarantees"
    - "learning to optimize training deep neural networks for interference management"
    - "federated learning for ultra reliable low latency v2v communications"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "user association and load balancing for massive mimo through deep learning"
    - "load balancing user association in millimeter wave mimo networks"

Connected through hyperedge HE-4 (induced by source paper "escaping saddle points with adaptive gradient methods"):
  with members:
    - "on the convergence of adam and beyond"
    - "on the convergence of adaptive gradient methods for nonconvex optimization"
    - "a sufficient condition for convergences of adam and rmsprop"
    - "the case for full matrix adaptive regularization"
    - "escaping saddle points in constrained optimization"
    - "adafactor adaptive learning rates with sublinear memory cost"
    - "on the convergence of a class of adam type algorithms for non convex optimization"
    - "a generic approach for escaping saddle points"

Connected through hyperedge HE-5 (induced by source paper "global convergence of adaptive gradient methods for an over parameterized neural network"):
  with members:
    - "on the convergence of adaptive gradient methods for nonconvex optimization"
    - "a sufficient condition for convergences of adam and rmsprop"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "closing the generalization gap of adaptive gradient methods in training deep neural networks"
    - "stochastic gradient descent optimizes over parameterized deep relu networks"
    - "signsgd compressed optimisation for non convex problems"
    - "a convergence theory for deep learning via over parameterization"

Connected through hyperedge HE-6 (induced by source paper "sadam a variant of adam for strongly convex functions"):
  with members:
    - "introduction to online convex optimization"
    - "on the convergence of adam and beyond"
    - "gadam genetic evolutionary adam for deep neural network optimization"
    - "adafactor adaptive learning rates with sublinear memory cost"
    - "on the convergence of a class of adam type algorithms for non convex optimization"
    - "convergence guarantees for rmsprop and adam in non convex optimization and an empirical comparison to nesterov acceleration"
    - "stronger baselines for trustable results in neural machine translation"
    - "draw a recurrent neural network for image generation"

Connected through hyperedge HE-7 (induced by source paper "on using machine learning to identify knowledge in api reference documentation"):
  with members:
    - "sentiment polarity detection for software development"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "natural language or not nlon a package for software engineering text analysis pipeline"
    - "easy over hard a case study on deep learning"
    - "an overview of gradient descent optimization algorithms"
    - "distributed representations of words and phrases and their compositionality"
    - "large scale multi label text classification revisiting neural networks"
    - "what should developers be aware of an empirical study on the directives of api documentation"

Connected through hyperedge HE-8 (induced by source paper "a cnn based approach to classify cricket bowlers based on their bowling actions"):
  with members:
    - "efficient processing of deep neural networks a tutorial and survey"
    - "understanding convolutional neural networks with a mathematical model"
    - "how transferable are features in deep neural networks"
    - "very deep convolutional networks for large scale image recognition"

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)