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: "a fast and simple algorithm for training neural probabilistic language models"
  Abstract: In spite of their superior performance, neural probabilistic language models (NPLMs) remain far less widely used than n-gram models due to their notoriously long training times, which are measured in weeks even for moderately-sized datasets. Training NPLMs is computationally expensive because they are explicitly normalized, which leads to having to consider all words in the vocabulary when computing the log-likelihood gradients. #R##N#We propose a fast and simple algorithm for training NPLMs based on noise-contrastive estimation, a newly introduced procedure for estimating unnormalized continuous distributions. We investigate the behaviour of the algorithm on the Penn Treebank corpus and show that it reduces the training times by more than an order of magnitude without affecting the quality of the resulting models. The algorithm is also more efficient and much more stable than importance sampling because it requires far fewer noise samples to perform well. #R##N#We demonstrate the scalability of the proposed approach by training several neural language models on a 47M-word corpus with a 80K-word vocabulary, obtaining state-of-the-art results on the Microsoft Research Sentence Completion Challenge dataset.

Connected through hyperedge HE-1 (induced by source paper "predictively encoded graph convolutional network for noise robust skeleton based action recognition"):
  with members:
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "driver drowsiness detection using condition adaptive representation learning framework"
    - "richlt activated graph convolutional network for action recognition with incomplete skeletons"
    - "learning graph convolutional network for skeleton based human action recognition by neural searching"
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "skeleton based action recognition with multi stream adaptive graph convolutional networks"
    - "hallucinating idt descriptors and i3d optical flow features for action recognition with cnns"
    - "densefusion 6d object pose estimation by iterative dense fusion"

Connected through hyperedge HE-2 (induced by source paper "word embedding algorithms as generalized low rank models and their canonical form"):
  with members:
    - "attention please a critical review of neural attention models in natural language processing"
    - "how large a vocabulary does text classification need a variational approach to vocabulary selection"
    - "deep learning for sentiment analysis a survey"
    - "code2vec learning distributed representations of code"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "morphosyntactic tagging with a meta bilstm model over context sensitive token encodings"
    - "generalizing word embeddings using bag of subwords"
    - "dynamic meta embeddings for improved sentence representations"

Connected through hyperedge HE-3 (induced by source paper "efficient contextual representation learning without softmax layer"):
  with members:
    - "von mises fisher loss for training sequence to sequence models with continuous outputs"
    - "multilingual constituency parsing with self attention and pre training"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "higher order coreference resolution with coarse to fine inference"
    - "an analysis of neural language modeling at multiple scales"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dissecting contextual word embeddings architecture and representation"

Connected through hyperedge HE-4 (induced by source paper "general probabilistic surface optimization and log density estimation"):
  with members:
    - "deep pdf probabilistic surface optimization and density estimation"
    - "spectral normalization for generative adversarial networks"
    - "understanding the effectiveness of lipschitz continuity in generative adversarial nets"
    - "mine mutual information neural estimation"
    - "began boundary equilibrium generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-5 (induced by source paper "anchor transform learning sparse representations of discrete objects"):
  with members:
    - "neural input search for large scale recommendation models"
    - "how large a vocabulary does text classification need a variational approach to vocabulary selection"
    - "word embedding based on low rank doubly stochastic matrix decomposition"
    - "bayesian compression for natural language processing"
    - "probabilistic fasttext for multi sense word embeddings"
    - "adaptive input representations for neural language modeling"
    - "towards learning sparsely used dictionaries with arbitrary supports"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "discrete flows invertible generative models of discrete data"):
  with members:
    - "integer discrete flows and lossless compression"
    - "waveglow a flow based generative network for speech synthesis"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "blockwise parallel decoding for deep autoregressive models"
    - "bayesian layers a module for neural network uncertainty"
    - "an analysis of neural language modeling at multiple scales"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "the importance of generation order in language modeling"

Connected through hyperedge HE-7 (induced by source paper "on variational bounds of mutual information"):
  with members:
    - "entropy and mutual information in models of deep neural networks"
    - "noise contrastive estimation and negative sampling for conditional models consistency and statistical efficiency"
    - "invariant representations without adversarial training"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "formal limitations on the measurement of mutual information"
    - "isolating sources of disentanglement in variational autoencoders"
    - "variational discriminator bottleneck improving imitation learning inverse rl and gans by constraining information flow"
    - "representation learning with contrastive predictive coding"

Connected through hyperedge HE-8 (induced by source paper "an empirical study of efficient asr rescoring with transformers"):
  with members:
    - "scalable multi corpora neural language models for asr"
    - "spanbert improving pre training by representing and predicting spans"
    - "efficient dynamic wfst decoding for personalized language models"
    - "transformer xl attentive language models beyond a fixed length context"
    - "language modeling with deep transformers"
    - "knowledge distillation for recurrent neural network language modeling with trust regularization"
    - "roberta a robustly optimized bert pretraining approach"
    - "bert pre training of deep bidirectional transformers for language understanding"

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.CL(Computation and Language)