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: "unsupervised neural hidden markov models"
  Abstract: In this work, we present the first results for neuralizing an Unsupervised Hidden Markov Model. We evaluate our approach on tag in- duction. Our approach outperforms existing generative models and is competitive with the state-of-the-art though with a simpler model easily extended to include additional context.

Connected through hyperedge HE-1 (induced by source paper "scalable hybrid hmm with gaussian process emission for sequential time series data clustering"):
  with members:
    - "neural non stationary spectral kernel"
    - "the ucr time series archive"
    - "toeplitz inverse covariance based clustering of multivariate time series data"

Connected through hyperedge HE-2 (induced by source paper "compound probabilistic context free grammars for grammar induction"):
  with members:
    - "scalable syntax aware language models using knowledge distillation"
    - "unsupervised recurrent neural network grammars"
    - "a multi task approach for disentangling syntax and semantics in sentence representations"
    - "unsupervised latent tree induction with deep inside outside recursive autoencoders"
    - "an imitation learning approach to unsupervised parsing"
    - "text generation with exemplar based adaptive decoding"
    - "controllable paraphrase generation with a syntactic exemplar"
    - "visually grounded neural syntax acquisition"

Connected through hyperedge HE-3 (induced by source paper "amortized bethe free energy minimization for learning mrfs"):
  with members:
    - "a tutorial on deep latent variable models of natural language"
    - "learning approximate inference networks for structured prediction"
    - "inference in probabilistic graphical models by graph neural networks"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "attention is all you need"
    - "neural variational inference and learning in undirected graphical models"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-4 (induced by source paper "fast structured decoding for sequence models"):
  with members:
    - "non autoregressive machine translation with auxiliary regularization"
    - "hint based training for non autoregressive machine translation"
    - "tensor2tensor for neural machine translation"
    - "a tutorial on deep latent variable models of natural language"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "fast decoding in sequence models using discrete latent variables"
    - "non autoregressive neural machine translation with enhanced decoder input"
    - "theory and experiments on vector quantized autoencoders"

Connected through hyperedge HE-5 (induced by source paper "a generative model for punctuation in dependency trees"):
  with members:
    - "the galactic dependencies treebanks getting more data by synthesizing new languages"
    - "recurrent neural network grammars"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "yara parser a fast and accurate dependency parser"
    - "improved semantic representations from tree structured long short term memory networks"
    - "adam a method for stochastic optimization"
    - "efficient estimation of word representations in vector space"
    - "thumbs up sentiment classification using machine learning techniques"

Connected through hyperedge HE-6 (induced by source paper "the standard coder a machine learning approach to measuring the effort required to produce source code change"):
  with members:
    - "do programmers work at night or during the weekend"
    - "predicting coding effort in projects containing xml code"

Connected through hyperedge HE-7 (induced by source paper "learning neural templates for text generation"):
  with members:
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "adversarially regularized autoencoders"
    - "learning to generate one sentence biographies from wikidata"
    - "neural language modeling by jointly learning syntax and lexicon"
    - "challenges in data to document generation"

Connected through hyperedge HE-8 (induced by source paper "unsupervised learning of syntactic structure with invertible neural projections"):
  with members:
    - "deep contextualized word representations"
    - "structvae tree structured latent variable models for semi supervised semantic parsing"
    - "i revnet deep invertible networks"
    - "dependency grammar induction with neural lexicalization and big training data"
    - "crf autoencoder for unsupervised dependency parsing"
    - "density estimation using real nvp"
    - "language as a latent variable discrete generative models for sentence compression"
    - "unsupervised pos induction with word embeddings"

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)