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: "muse modularizing unsupervised sense embeddings"
  Abstract: This paper proposes to address the word sense ambiguity issue in an unsupervised manner, where word sense representations are learned along a word sense selection mechanism given contexts. Prior work focused on designing a single model to deliver both mechanisms, and thus suffered from either coarse-grained representation learning or inefficient sense selection. The proposed modular approach, MUSE, implements flexible modules to optimize distinct mechanisms, achieving the first purely sense-level representation learning system with linear-time sense selection. We leverage reinforcement learning to enable joint training on the proposed modules, and introduce various exploration techniques on sense selection for better robustness. The experiments on benchmark data show that the proposed approach achieves the state-of-the-art performance on synonym selection as well as on contextual word similarities in terms of MaxSimC.

Connected through hyperedge HE-1 (induced by source paper "cross topic distributional semantic representations via unsupervised mappings"):
  with members:
    - "inducing and embedding senses with scaled gumbel softmax"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "learning rare word representations using semantic bridging"
    - "a mixture model for learning multi sense word embeddings"
    - "the umd neural machine translation systems at wmt17 bandit learning task"
    - "de conflated semantic representations"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "efficient non parametric estimation of multiple embeddings per word in vector space"

Connected through hyperedge HE-2 (induced by source paper "secret semantically enhanced classification of real world tasks"):
  with members:
    - "an overview of embedding models of entities and relationships for knowledge base completion"
    - "cnn rnn a unified framework for multi label image classification"
    - "open question answering with weakly supervised embedding models"
    - "deep visual semantic alignments for generating image descriptions"
    - "zero shot learning through cross modal transfer"
    - "efficient estimation of word representations in vector space"
    - "word emdeddings through hellinger pca"
    - "machine learning in automated text categorization"

Connected through hyperedge HE-3 (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-4 (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"
    - "de conflated semantic representations"

Connected through hyperedge HE-5 (induced by source paper "xsense learning sense separated sparse representations and textual definitions for explainable word sense networks"):
  with members:
    - "learning to compute word embeddings on the fly"
    - "spine sparse interpretable neural embeddings"
    - "word translation without parallel data"
    - "linear algebraic structure of word senses with applications to polysemy"
    - "the mythos of model interpretability"
    - "skip thought vectors"
    - "learning to understand phrases by embedding the dictionary"
    - "sparse overcomplete word vector representations"

Connected through hyperedge HE-6 (induced by source paper "cluse cross lingual unsupervised sense embeddings"):
  with members:
    - "beyond bilingual multi sense word embeddings using multilingual context"
    - "word translation without parallel data"
    - "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"
    - "learning bilingual word representations by marginalizing alignments"
    - "multilingual models for compositional distributed semantics"
    - "an autoencoder approach to learning bilingual word representations"

Connected through hyperedge HE-7 (induced by source paper "understanding and improving multi sense word embeddings via extended robust principal component analysis"):
  with members:
    - "real multi sense or pseudo multi sense an approach to improve word representation"
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "do multi sense embeddings improve natural language understanding"

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)