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 pos induction with word embeddings"
  Abstract: Unsupervised word embeddings have been shown to be valuable as features in supervised learning problems; however, their role in unsupervised problems has been less thoroughly explored. In this paper, we show that embeddings can likewise add value to the problem of unsupervised POS induction. In two representative models of POS induction, we replace multinomial distributions over the vocabulary with multivariate Gaussian distributions over word embeddings and observe consistent improvements in eight languages. We also analyze the effect of various choices while inducing word embeddings on "downstream" POS induction results.

Connected through hyperedge HE-1 (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-2 (induced by source paper "named entity recognition only from word embeddings"):
  with members:
    - "knowledge augmented language model and its application to unsupervised named entity recognition"
    - "design challenges and misconceptions in neural sequence labeling"
    - "reinforcement learning for relation classification from noisy data"
    - "ncrf an open source neural sequence labeling toolkit"
    - "unsupervised learning of syntactic structure with invertible neural projections"
    - "deep active learning for named entity recognition"
    - "swellshark a generative model for biomedical named entity recognition without labeled data"
    - "empower sequence labeling with task aware neural language model"

Connected through hyperedge HE-3 (induced by source paper "syntactic interchangeability in word embedding models"):
  with members:
    - "a survey of word embeddings evaluation methods"
    - "concept2vec metrics for evaluating quality of embeddings for ontological concepts"
    - "how to evaluate word embeddings on importance of data efficiency and simple supervised tasks"
    - "redefining context windows for word embedding models an experimental study"
    - "improving reliability of word similarity evaluation by redesigning annotation task and performance measure"
    - "enriching word vectors with subword information"
    - "a primer on neural network models for natural language processing"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"

Connected through hyperedge HE-4 (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 neural hidden markov models"

Connected through hyperedge HE-5 (induced by source paper "semantic structure and interpretability of word embeddings"):
  with members:
    - "rotations and interpretability of word embeddings the case of the russian language"
    - "hyperlex a large scale evaluation of graded lexical entailment"
    - "enriching word vectors with subword information"
    - "linear algebraic structure of word senses with applications to polysemy"
    - "sparse overcomplete word vector representations"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-6 (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"
    - "deep learning and the information bottleneck principle"
    - "stochastic optimization for deep cca via nonlinear orthogonal iterations"
    - "conditional random field autoencoders for unsupervised structured prediction"

Connected through hyperedge HE-7 (induced by source paper "redefining context windows for word embedding models an experimental study"):
  with members:
    - "neural architectures for named entity recognition"
    - "a primer on neural network models for natural language processing"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-8 (induced by source paper "sparse coding of neural word embeddings for multilingual sequence labeling"):
  with members:
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "compressing neural language models by sparse word representations"
    - "big data small data in domain out of domain known word unknown word the impact of word representation on sequence labelling tasks"
    - "sparse overcomplete word vector representations"
    - "learning word representations with hierarchical sparse coding"
    - "polyglot distributed word representations for multilingual nlp"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

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)