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: "rmsprop and equilibrated adaptive learning rates for non convex optimization"
  Abstract: Parameter-specific adaptive learning rate methods are computationally efficient ways to reduce the ill-conditioning problems encountered when training large deep networks. Following recent work that strongly suggests that most of the critical points encountered when training such networks are saddle points, we find how considering the presence of negative eigenvalues of the Hessian could help us design better suited adaptive learning rate schemes, i.e., diagonal preconditioners. We show that the optimal preconditioner is based on taking the absolute value of the Hessian's eigenvalues, which is not what Newton and classical preconditioners like Jacobi's do. In this paper, we propose a novel adaptive learning rate scheme based on the equilibration preconditioner and show that RMSProp approximates it, which may explain some of its success in the presence of saddle points. Whereas RMSProp is a biased estimator of the equilibration preconditioner, the proposed stochastic estimator, ESGD, is unbiased and only adds a small percentage to computing time. We find that both schemes yield very similar step directions but that ESGD sometimes surpasses RMSProp in terms of convergence speed, always clearly improving over plain stochastic gradient descent.

Connected through hyperedge HE-1 (induced by source paper "domain independent dominance of adaptive methods"):
  with members:
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "on the convergence of adam and beyond"
    - "on the convergence of adabound and its connection to sgd"
    - "an analysis of neural language modeling at multiple scales"
    - "adashift decorrelation and convergence of adaptive learning rate methods"
    - "on the convergence of a class of adam type algorithms for non convex optimization"
    - "attention is all you need"
    - "decoupled weight decay regularization"

Connected through hyperedge HE-2 (induced by source paper "learning numeracy binary arithmetic with neural turing machines"):
  with members:
    - "implementing neural turing machines"
    - "lie access neural turing machine"
    - "dynamic neural turing machine with soft and hard addressing schemes"
    - "learning simple algorithms from examples"
    - "deep learning in neural networks"
    - "structured memory for neural turing machines"
    - "neural gpus learn algorithms"
    - "neural turing machines"

Connected through hyperedge HE-3 (induced by source paper "deep learning to address candidate generation and cold start challenges in recommender systems a research survey"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "wide deep learning for recommender systems"
    - "deep learning in neural networks"
    - "the continuous cold start problem in e commerce recommender systems"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "the use of machine learning algorithms in recommender systems a systematic review"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "baselines for reinforcement learning in text games"):
  with members:
    - "using reinforcement learning to learn how to play text based games"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "prioritized experience replay"
    - "language understanding for text based games using deep reinforcement learning"
    - "learning to win by reading manuals in a monte carlo framework"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "modeling the intensity function of point process via recurrent neural networks"):
  with members:
    - "predicting clinical events by combining static and dynamic information using recurrent neural networks"
    - "recurrent neural networks for multivariate time series with missing values"
    - "recurrent neural networks for driver activity anticipation via sensory fusion architecture"
    - "equilibrated adaptive learning rates for non convex optimization"
    - "doctor ai predicting clinical events via recurrent neural networks"
    - "scheduled sampling for sequence prediction with recurrent neural networks"
    - "draw a recurrent neural network for image generation"
    - "a holistic approach to log data analysis in high performance computing systems the case of ibm blue gene q"

Connected through hyperedge HE-6 (induced by source paper "strategies for conceptual change in convolutional neural networks"):
  with members:
    - "deep reinforcement learning with double q learning"
    - "playing atari with deep reinforcement learning"
    - "representation learning a review and new perspectives"
    - "improving neural networks by preventing co adaptation of feature detectors"
    - "frustratingly easy domain adaptation"
    - "domain adaptation for statistical classifiers"
    - "non negative matrix factorization with sparseness constraints"

Connected through hyperedge HE-7 (induced by source paper "land cover classification via multitemporal spatial data by deep recurrent neural networks"):
  with members:
    - "lstm a search space odyssey"
    - "conditional image generation with pixelcnn decoders"
    - "assessing the ability of lstms to learn syntax sensitive dependencies"
    - "speech recognition with deep recurrent neural networks"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-8 (induced by source paper "end to end sequence labeling via bi directional lstm cnns crf"):
  with members:
    - "harnessing deep neural networks with logic rules"
    - "unsupervised ranking model for entity coreference resolution"
    - "multi task cross lingual sequence tagging from scratch"
    - "improving named entity recognition for chinese social media with word segmentation representation learning"
    - "neural architectures for named entity recognition"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "bidirectional lstm crf models for sequence tagging"
    - "transition based dependency parsing with stack long short term memory"

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)