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: "gaussian error linear units gelus"
  Abstract: We propose the Gaussian Error Linear Unit (GELU), a high-performing neural network activation function. The GELU nonlinearity is the expected transformation of a stochastic regularizer which randomly applies the identity or zero map to a neuron's input. The GELU nonlinearity weights inputs by their magnitude, rather than gates inputs by their sign as in ReLUs. We perform an empirical evaluation of the GELU nonlinearity against the ReLU and ELU activations and find performance improvements across all considered computer vision, natural language processing, and speech tasks.

Connected through hyperedge HE-1 (induced by source paper "learning home crowdsourced training of large neural networks using decentralized mixture of experts"):
  with members:
    - "scaling laws for neural language models"
    - "zero memory optimization towards training a trillion parameter models"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "natural compression for distributed deep learning"
    - "defending against neural fake news"
    - "large scale learning of general visual representations for transfer"
    - "optimizing network performance for distributed dnn training on gpu clusters imagenet alexnet training in 1 5 minutes"
    - "pipemare asynchronous pipeline parallel dnn training"

Connected through hyperedge HE-2 (induced by source paper "lightweight convolutional representations for on device natural language processing"):
  with members:
    - "are sixteen heads really better than one"
    - "fixed point convolutional neural network for real time video processing in fpga"
    - "the state of sparsity in deep neural networks"
    - "accelerator aware pruning for convolutional neural networks"
    - "rethinking the value of network pruning"
    - "convolutional sequence to sequence learning"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "language modeling with gated convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "attentive item2vec neural attentive user representations"):
  with members:
    - "neural attentive multiview machines"
    - "scalable attentive sentence pair modeling via distilled sentence embedding"
    - "cb2cf a neural multiview content to collaborative filtering model for completely cold item recommendations"
    - "evaluation of session based recommendation algorithms"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "atrank an attention based user behavior modeling framework for recommendation"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "avoiding kernel fixed points computing with elu and gelu infinite networks"):
  with members:
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "richer priors for infinitely wide multi layer perceptrons"
    - "exchangeability and kernel invariance in trained mlps"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "searching for activation functions"
    - "sigmoid weighted linear units for neural network function approximation in reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "convert efficient and accurate conversational representations from transformers"):
  with members:
    - "deep retrieval based dialogue systems a short review"
    - "q8bert quantized 8bit bert"
    - "designing for health chatbots"
    - "are sixteen heads really better than one"
    - "dialog system technology challenge 7"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "multiwoz 2 1 multi domain dialogue state corrections and state tracking baselines"
    - "extreme language model compression with optimal subwords and shared projections"

Connected through hyperedge HE-6 (induced by source paper "albert a lite bert for self supervised learning of language representations"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "spanbert improving pre training by representing and predicting spans"
    - "well read students learn better on the importance of pre training compact models"
    - "transformer xl attentive language models beyond a fixed length context"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "structbert incorporating language structures into pre training for deep language understanding"
    - "generating long sequences with sparse transformers"

Connected through hyperedge HE-7 (induced by source paper "unified language model pre training for natural language understanding and generation"):
  with members:
    - "fine tune bert for extractive summarization"
    - "learning and evaluating general linguistic intelligence"
    - "pre trained language model representations for language generation"
    - "mass masked sequence to sequence pre training for language generation"
    - "multi task deep neural networks for natural language understanding"
    - "cloze driven pretraining of self attention networks"
    - "bert has a mouth and it must speak bert as a markov random field language model"
    - "cross lingual natural language generation via pre training"

Connected through hyperedge HE-8 (induced by source paper "roberta a robustly optimized bert pretraining approach"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "ernie enhanced representation through knowledge integration"
    - "spanbert improving pre training by representing and predicting spans"
    - "a surprisingly robust trick for the winograd schema challenge"
    - "mass masked sequence to sequence pre training for language generation"
    - "multi task deep neural networks for natural language understanding"
    - "cloze driven pretraining of self attention networks"
    - "defending against neural fake news"

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)