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: "conditional random field autoencoders for unsupervised structured prediction"
  Abstract: We introduce a framework for unsupervised learning of structured predictors with overlapping, global features. Each input's latent representation is predicted conditional on the observable data using a feature-rich conditional random field. Then a reconstruction of the input is (re)generated, conditional on the latent structure, using models for which maximum likelihood estimation has a closed-form. Our autoencoder formulation enables efficient learning without making unrealistic independence assumptions or restricting the kinds of features that can be used. We illustrate insightful connections to traditional autoencoders, posterior regularization and multi-view learning. We show competitive results with instantiations of the model for two canonical NLP tasks: part-of-speech induction and bitext word alignment, and show that training our model can be substantially more efficient than comparable feature-rich baselines.

Connected through hyperedge HE-1 (induced by source paper "unsupervised recurrent neural network grammars"):
  with members:
    - "unsupervised latent tree induction with deep inside outside recursive autoencoders"
    - "cooperative learning of disjoint syntax and semantics"
    - "finding syntax in human encephalography with beam search"
    - "on tree based neural sentence modeling"
    - "unsupervised grammar induction with depth bounded pcfg"
    - "backpropagating through structured argmax using a spigot"
    - "inducing grammars with and for neural machine translation"
    - "latent tree learning with differentiable parsers shift reduce parsing and chart parsing"

Connected through hyperedge HE-2 (induced by source paper "a structured variational autoencoder for contextual morphological inflection"):
  with members:
    - "multi space variational encoder decoders for semi supervised labeled sequence transduction"
    - "enriching word vectors with subword information"
    - "effective approaches to attention based neural machine translation"
    - "morphological inflection generation using character sequence to sequence learning"
    - "reweighted wake sleep"
    - "sequence to sequence learning with neural networks"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-3 (induced by source paper "mutual information maximization for simple and accurate part of speech induction"):
  with members:
    - "unsupervised learning of syntactic structure with invertible neural projections"
    - "representation learning with contrastive predictive coding"
    - "unsupervised neural hidden markov models"
    - "unsupervised pos induction with word embeddings"
    - "deep learning and the information bottleneck principle"
    - "stochastic optimization for deep cca via nonlinear orthogonal iterations"

Connected through hyperedge HE-4 (induced by source paper "grounding of textual phrases in images by reconstruction"):
  with members:
    - "yfcc100m the new data in multimedia research"
    - "generation and comprehension of unambiguous object descriptions"
    - "webly supervised learning of convolutional networks"
    - "learning deep structure preserving image text embeddings"
    - "aligning where to see and what to tell image caption with region based attention and scene factorization"
    - "show attend and tell neural image caption generation with visual attention"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast r cnn"

Connected through hyperedge HE-5 (induced by source paper "bilingual learning of multi sense embeddings with discrete autoencoders"):
  with members:
    - "learning to represent words in context with multilingual supervision"
    - "breaking sticks and ambiguities with adaptive skip gram"
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "do multi sense embeddings improve natural language understanding"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "embedding word similarity with neural machine translation"
    - "leveraging monolingual data for crosslingual compositional word representations"
    - "multilingual models for compositional distributed semantics"

Connected through hyperedge HE-6 (induced by source paper "semantic parsing with semi supervised sequential autoencoders"):
  with members:
    - "bilingual learning of multi sense embeddings with discrete autoencoders"
    - "language to logical form with neural attention"
    - "data recombination for neural semantic parsing"
    - "listen attend and walk neural mapping of navigational instructions to action sequences"
    - "alignment based compositional semantics for instruction following"
    - "on using monolingual corpora in neural machine translation"
    - "type driven incremental semantic parsing with polymorphism"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-7 (induced by source paper "semi supervised learning for neural machine translation"):
  with members:
    - "improving neural machine translation models with monolingual data"
    - "on using monolingual corpora in neural machine translation"
    - "semi supervised learning with deep generative models"
    - "neural machine translation by jointly learning to align and translate"
    - "on using very large target vocabulary for neural machine translation"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"
    - "addressing the rare word problem in neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "hybrid collaborative filtering with autoencoders"):
  with members:
    - "hybrid collaborative filtering with neural networks"
    - "neural network matrix factorization"
    - "collaborative deep learning for recommender systems"
    - "ordinal boltzmann machines for collaborative filtering"
    - "a comparative study of collaborative filtering algorithms"

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)