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: "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
  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. This stochastic regularizer is comparable to nonlinearities aided by dropout, but it removes the need for a traditional nonlinearity. The connection between the GELU and the stochastic regularizer suggests a new probabilistic understanding of nonlinearities. We perform an empirical evaluation of the GELU nonlinearity against the ReLU and ELU activations and find performance improvements across all tasks.

Connected through hyperedge HE-1 (induced by source paper "xgpt cross modal generative pre training for image captioning"):
  with members:
    - "lxmert learning cross modality encoder representations from transformers"
    - "uniter learning universal image text representations"
    - "fusion of detected objects in text for visual question answering"
    - "unicoder a universal language encoder by pre training with multiple cross lingual tasks"
    - "unicoder vl a universal encoder for vision and language by cross modal pre training"
    - "attention on attention for image captioning"
    - "mass masked sequence to sequence pre training for language generation"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"

Connected through hyperedge HE-2 (induced by source paper "fdftnet facing off fake images using fake detection fine tuning network"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "sliced wasserstein generative models"
    - "few shot adversarial learning of realistic neural talking head models"
    - "searching for mobilenetv3"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "a quantization friendly separable convolution for mobilenets"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "meta transfer learning for few shot learning"

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"
    - "atrank an attention based user behavior modeling framework for recommendation"
    - "attention is all you need"
    - "neural attentive session based recommendation"

Connected through hyperedge HE-4 (induced by source paper "rezero is all you need fast convergence at large depth"):
  with members:
    - "batch normalization biases deep residual networks towards shallow paths"
    - "on layer normalization in the transformer architecture"
    - "dynamical isometry and a mean field theory of lstms and grus"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "character level language modeling with deeper self attention"
    - "resurrecting the sigmoid in deep learning through dynamical isometry theory and practice"
    - "self normalizing neural networks"
    - "mean field residual networks on the edge of chaos"

Connected through hyperedge HE-5 (induced by source paper "language models are an effective patient representation learning technique for electronic health record data"):
  with members:
    - "mime multilevel medical embedding of electronic health records for predictive healthcare"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "leveraging clinical time series data for prediction a cautionary tale"
    - "universal language model fine tuning for text classification"
    - "scalable and accurate deep learning for electronic health records"
    - "improving palliative care with deep learning"
    - "medical concept representation learning from electronic health records and its application on heart failure prediction"

Connected through hyperedge HE-6 (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"
    - "searching for activation functions"
    - "sigmoid weighted linear units for neural network function approximation in reinforcement learning"
    - "invariance of weight distributions in rectified mlps"

Connected through hyperedge HE-7 (induced by source paper "goldilocks neural networks"):
  with members:
    - "improving deep learning by inverse square root linear units isrlus"
    - "sigmoid weighted linear units for neural network function approximation in reinforcement learning"
    - "swish a self gated activation function"
    - "a continuum among logarithmic linear and exponential functions and its potential to improve generalization in neural networks"
    - "deep learning with s shaped rectified linear activation units"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "empirical evaluation of rectified activations in convolutional network"

Connected through hyperedge HE-8 (induced by source paper "keyphrase extraction with span based feature representations"):
  with members:
    - "keyphrase generation with correlation constraints"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"
    - "deep keyphrase generation"
    - "a structured self attentive sentence embedding"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "modeling coverage for neural machine translation"

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)