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: "training restricted boltzmann machines on word observations"
  Abstract: The restricted Boltzmann machine (RBM) is a flexible tool for modeling complex data, however there have been significant computational difficulties in using RBMs to model high-dimensional multinomial observations. In natural language processing applications, words are naturally modeled by K-ary discrete distributions, where K is determined by the vocabulary size and can easily be in the hundreds of thousands. The conventional approach to training RBMs on word observations is limited because it requires sampling the states of K-way softmax visible units during block Gibbs updates, an operation that takes time linear in K. In this work, we address this issue by employing a more general class of Markov chain Monte Carlo operators on the visible units, yielding updates with computational complexity independent of K. We demonstrate the success of our approach by training RBMs on hundreds of millions of word n-grams using larger vocabularies than previously feasible and using the learned features to improve performance on chunking and sentiment classification tasks, achieving state-of-the-art results on the latter.

Connected through hyperedge HE-1 (induced by source paper "state regularized recurrent neural networks"):
  with members:
    - "context free transductions with neural stacks"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "an empirical evaluation of rule extraction from recurrent neural networks"
    - "attention is all you need"
    - "frustratingly short attention spans in neural language modeling"
    - "automatic rule extraction from long short term memory networks"

Connected through hyperedge HE-2 (induced by source paper "large scale collaborative filtering with product embeddings"):
  with members:
    - "deep sets"
    - "structured attention networks"
    - "starspace embed all the things"
    - "attention is all you need"
    - "a simple neural network module for relational reasoning"
    - "deep cross network for ad click predictions"
    - "specializing joint representations for the task of product recommendation"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "contextual explanation networks"):
  with members:
    - "a unified approach to interpreting model predictions"
    - "practical black box attacks against machine learning"
    - "rationalizing neural predictions"
    - "why should i trust you explaining the predictions of any classifier"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-4 (induced by source paper "dependency sensitive convolutional neural networks for modeling sentences and documents"):
  with members:
    - "multichannel variable size convolution for sentence classification"
    - "a hierarchical neural autoencoder for paragraphs and documents"
    - "improved semantic representations from tree structured long short term memory networks"
    - "dependency based convolutional neural networks for sentence embedding"
    - "semi supervised sequence learning"
    - "semantically conditioned lstm based natural language generation for spoken dialogue systems"
    - "distributed representations of sentences and documents"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-5 (induced by source paper "a new data representation based on training data characteristics to extract drug named entity in medical text"):
  with members:
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-6 (induced by source paper "learning document embeddings by predicting n grams for sentiment classification of long movie reviews"):
  with members:
    - "skip thought vectors"
    - "distributed representations of sentences and documents"
    - "effective use of word order for text categorization with convolutional neural networks"
    - "ensemble of generative and discriminative techniques for sentiment analysis of movie reviews"
    - "a convolutional neural network for modelling sentences"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-7 (induced by source paper "a generative model for multi dialect representation"):
  with members:
    - "deep learning in neural networks"
    - "improved deep convolutional neural network for online handwritten chinese character recognition using domain specific knowledge"
    - "conditional restricted boltzmann machines for structured output prediction"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-8 (induced by source paper "efficient learning for undirected topic models"):
  with members:
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "modeling documents with deep boltzmann machines"
    - "a fast and simple algorithm for training neural probabilistic language models"

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)