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: "adafactor adaptive learning rates with sublinear memory cost"
  Abstract: In several recently proposed stochastic optimization methods (e.g. RMSProp, Adam, Adadelta), parameter updates are scaled by the inverse square roots of exponential moving averages of squared past gradients. Maintaining these per-parameter second-moment estimators requires memory equal to the number of parameters. For the case of neural network weight matrices, we propose maintaining only the per-row and per-column sums of these moving averages, and estimating the per-parameter second moments based on these sums. We demonstrate empirically that this method produces similar results to the baseline. Secondly, we show that adaptive methods can produce larger-than-desired updates when the decay rate of the second moment accumulator is too slow. We propose update clipping and a gradually increasing decay rate scheme as remedies. Combining these methods and dropping momentum, we achieve comparable results to the published Adam regime in training the Transformer model on the WMT 2014 English-German machine translation task, while using very little auxiliary storage in the optimizer. Finally, we propose scaling the parameter updates based on the scale of the parameters themselves.

Connected through hyperedge HE-1 (induced by source paper "disentangling adaptive gradient methods from learning rates"):
  with members:
    - "the step decay schedule a near optimal geometrically decaying learning rate procedure for least squares"
    - "extreme tensoring for low memory preconditioning"
    - "on empirical comparisons of optimizers for deep learning"
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "stochastic gradient methods with layer wise adaptive moments for training of deep networks"
    - "escaping saddle points with adaptive gradient methods"
    - "an exponential learning rate schedule for deep learning"
    - "deepobs a deep learning optimizer benchmark suite"

Connected through hyperedge HE-2 (induced by source paper "towards a human like open domain chatbot"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "better automatic evaluation of open domain dialogue systems with contextualized embeddings"
    - "the curious case of neural text degeneration"
    - "unifying human and statistical evaluation for natural language generation"
    - "human and automatic detection of generated text"
    - "ctrl a conditional transformer language model for controllable generation"
    - "jointly optimizing diversity and relevance in neural response generation"
    - "the evolved transformer"

Connected through hyperedge HE-3 (induced by source paper "scaling laws for neural language models"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "which algorithmic choices matter at which batch sizes insights from a noisy quadratic model"
    - "growing a brain fine tuning by increasing model capacity"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "autogrow automatic layer growing in deep convolutional networks"
    - "a constructive prediction of the generalization error across scales"
    - "universal transformers"
    - "generating long sequences with sparse transformers"

Connected through hyperedge HE-4 (induced by source paper "how much knowledge can you pack into the parameters of a language model"):
  with members:
    - "realm retrieval augmented language model pre training"
    - "boolq exploring the surprising difficulty of natural yes no questions"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "language models as knowledge bases"
    - "latent retrieval for weakly supervised open domain question answering"
    - "knowledge guided text retrieval and reading for open domain question answering"
    - "how can we know what language models know"
    - "frustratingly easy natural question answering"

Connected through hyperedge HE-5 (induced by source paper "momentum improves normalized sgd"):
  with members:
    - "reducing the variance in online optimization by transporting past gradients"
    - "memory efficient adaptive optimization"
    - "momentum based variance reduction in non convex sgd"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "on the convergence of adam and beyond"
    - "stochastic nested variance reduction for nonconvex optimization"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"

Connected through hyperedge HE-6 (induced by source paper "glu variants improve transformer"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "superglue a stickier benchmark for general purpose language understanding systems"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "attention is all you need"
    - "searching for activation functions"
    - "swish a self gated activation function"
    - "squad 100 000 questions for machine comprehension of text"

Connected through hyperedge HE-7 (induced by source paper "learning accurate integer transformer machine translation models"):
  with members:
    - "asymmetric valleys beyond sharp and flat local minima"
    - "efficient 8 bit quantization of transformer neural machine language translation model"
    - "training tips for the transformer model"
    - "marian cost effective high quality neural machine translation in c"
    - "attention is all you need"
    - "deep residual learning for image recognition"
    - "neural machine translation by jointly learning to align and translate"
    - "estimating or propagating gradients through stochastic neurons for conditional computation"

Connected through hyperedge HE-8 (induced by source paper "evaluating sequence to sequence learning models for if then program synthesis"):
  with members:
    - "joey nmt a minimalist nmt toolkit for novices"
    - "opennmt neural machine translation toolkit"
    - "interactive semantic parsing for if then recipes via hierarchical reinforcement learning"
    - "attention is all you need"
    - "latent attention for if then program synthesis"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"

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)