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: "learning to represent words in context with multilingual supervision"
  Abstract: We present a neural network architecture based on bidirectional LSTMs to compute representations of words in the sentential contexts. These context-sensitive word representations are suitable for, e.g., distinguishing different word senses and other context-modulated variations in meaning. To learn the parameters of our model, we use cross-lingual supervision, hypothesizing that a good representation of a word in context will be one that is sufficient for selecting the correct translation into a second language. We evaluate the quality of our representations as features in three downstream tasks: prediction of semantic supersenses (which assign nouns and verbs into a few dozen semantic classes), low resource machine translation, and a lexical substitution task, and obtain state-of-the-art results on all of these.

Connected through hyperedge HE-1 (induced by source paper "learning to embed words in context for syntactic tasks"):
  with members:
    - "semi supervised sequence tagging with bidirectional language models"
    - "context dependent word representation for neural machine translation"
    - "semi supervised preposition sense disambiguation using multilingual data"
    - "a hierarchical neural autoencoder for paragraphs and documents"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "do multi sense embeddings improve natural language understanding"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-2 (induced by source paper "beyond bilingual multi sense word embeddings using multilingual context"):
  with members:
    - "cross lingual models of word embeddings an empirical comparison"
    - "problems with evaluation of word embeddings using word similarity tasks"
    - "breaking sticks and ambiguities with adaptive skip gram"
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "a state of the art of word sense induction a way towards word sense disambiguation for under resourced languages"
    - "multilingual distributed representations without word alignment"
    - "similarity of semantic relations"

Connected through hyperedge HE-3 (induced by source paper "bilingual learning of multi sense embeddings with discrete autoencoders"):
  with members:
    - "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"
    - "conditional random field autoencoders for unsupervised structured prediction"

Connected through hyperedge HE-4 (induced by source paper "semi supervised preposition sense disambiguation using multilingual data"):
  with members:
    - "bilingual learning of multi sense embeddings with discrete autoencoders"
    - "visualizing and understanding recurrent networks"
    - "a large annotated corpus for learning natural language inference"
    - "polyglot distributed word representations for multilingual nlp"
    - "an inventory of preposition relations"
    - "distributed representations of words and phrases and their compositionality"
    - "multilingual distributed representations without word alignment"
    - "the jrc acquis a multilingual aligned parallel corpus with 20 languages"

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)