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: "bilingual learning of multi sense embeddings with discrete autoencoders"
  Abstract: We present an approach to learning multi-sense word embeddings relying both on monolingual and bilingual information. Our model consists of an encoder, which uses monolingual and bilingual context (i.e. a parallel sentence) to choose a sense for a given word, and a decoder which predicts context words based on the chosen sense. The two components are estimated jointly. We observe that the word representations induced from bilingual data outperform the monolingual counterparts across a range of evaluation tasks, even though crosslingual information is not available at test time.

Connected through hyperedge HE-1 (induced by source paper "a variational approach to weakly supervised document level multi aspect sentiment classification"):
  with members:
    - "variational knowledge graph reasoning"
    - "weakly supervised neural text classification"
    - "variational reasoning for question answering with knowledge graph"
    - "interactive attention networks for aspect level sentiment classification"
    - "recursive neural conditional random fields for aspect based sentiment analysis"
    - "aspect level sentiment classification with deep memory network"
    - "rationalizing neural predictions"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-2 (induced by source paper "from word to sense embeddings a survey on vector representations of meaning"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "seven augmenting word embeddings with unsupervised relation vectors"
    - "incorporating glosses into neural word sense disambiguation"
    - "deep contextualized word representations"
    - "wic 10 000 example pairs for evaluating context sensitive representations"
    - "knowledge based word sense disambiguation using topic models"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"

Connected through hyperedge HE-3 (induced by source paper "inducing and embedding senses with scaled gumbel softmax"):
  with members:
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "deep contextualized word representations"
    - "wic 10 000 example pairs for evaluating context sensitive representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"
    - "probabilistic fasttext for multi sense word embeddings"
    - "a mixture model for learning multi sense word embeddings"
    - "muse modularizing unsupervised sense embeddings"

Connected through hyperedge HE-4 (induced by source paper "graph based bilingual word embedding for statistical machine translation"):
  with members:
    - "syntax directed attention for neural machine translation"
    - "mimicking word embeddings using subword rnns"
    - "chunk based bi scale decoder for neural machine translation"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "a simple regularization based algorithm for learning cross domain word embeddings"
    - "six challenges for neural machine translation"
    - "cross lingual models of word embeddings an empirical comparison"
    - "vocabulary manipulation for neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "learning gender neutral word embeddings"):
  with members:
    - "gender bias in coreference resolution"
    - "deep contextualized word representations"
    - "word embeddings quantify 100 years of gender and ethnic stereotypes"
    - "gender bias in coreference resolution evaluation and debiasing methods"
    - "end to end neural coreference resolution"
    - "beyond bilingual multi sense word embeddings using multilingual context"
    - "men also like shopping reducing gender bias amplification using corpus level constraints"
    - "semantics derived automatically from language corpora contain human like biases"

Connected through hyperedge HE-6 (induced by source paper "aspem embedding learning by aspects in heterogeneous information networks"):
  with members:
    - "struc2vec learning node representations from structural identity"
    - "learning edge representations via low rank asymmetric projections"
    - "prep path based relevance from a probabilistic perspective in heterogeneous information networks"
    - "node2vec scalable feature learning for networks"
    - "linear algebraic structure of word senses with applications to polysemy"
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "a survey of heterogeneous information network analysis"
    - "pte predictive text embedding through large scale heterogeneous text networks"

Connected through hyperedge HE-7 (induced by source paper "you shall know the most frequent sense by the company it keeps"):
  with members:
    - "de conflated semantic representations"
    - "embedding words and senses together via joint knowledge enhanced training"
    - "linear algebraic structure of word senses with applications to polysemy"
    - "autoextend extending word embeddings to embeddings for synsets and lexemes"
    - "efficient estimation of word representations in vector space"
    - "exploiting similarities among languages for machine translation"

Connected through hyperedge HE-8 (induced by source paper "a simple approach to learn polysemous word embeddings"):
  with members:
    - "beyond bilingual multi sense word embeddings using multilingual context"
    - "linear algebraic structure of word senses with applications to polysemy"
    - "breaking sticks and ambiguities with adaptive skip gram"
    - "skip thought vectors"
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "do multi sense embeddings improve natural language understanding"
    - "distributed representations of sentences and documents"
    - "efficient estimation of word representations in vector space"

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)