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: "a mixture model for learning multi sense word embeddings"
  Abstract: Word embeddings are now a standard technique for inducing meaning representations for words. For getting good representations, it is important to take into account different senses of a word. In this paper, we propose a mixture model for learning multi-sense word embeddings. Our model generalizes the previous works in that it allows to induce different weights of different senses of a word. The experimental results show that our model outperforms previous models on standard evaluation tasks.

Connected through hyperedge HE-1 (induced by source paper "bertscore evaluating text generation with bert"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "fine tune bert for extractive summarization"
    - "alternative weighting schemes for elmo embeddings"
    - "deep reinforcement learning with distributional semantic rewards for abstractive summarization"
    - "pay less attention with lightweight and dynamic convolutions"
    - "simple applications of bert for ad hoc document retrieval"
    - "unifying human and statistical evaluation for natural language generation"
    - "scibert a pretrained language model for scientific text"

Connected through hyperedge HE-2 (induced by source paper "multi sense definition modeling using word sense decompositions"):
  with members:
    - "incorporating sememes into chinese definition modeling"
    - "conditional generators of words definitions"
    - "learning to explain non standard english words and phrases"
    - "de conflated semantic representations"
    - "definition modeling learning to define word embeddings in natural language"
    - "linear algebraic structure of word senses with applications to polysemy"
    - "dynamic memory networks for visual and textual question answering"
    - "breaking sticks and ambiguities with adaptive skip gram"

Connected through hyperedge HE-3 (induced by source paper "a probabilistic framework for learning domain specific hierarchical word embeddings"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "word embeddings quantify 100 years of gender and ethnic stereotypes"
    - "neural collaborative filtering"
    - "learned in translation contextualized word vectors"
    - "structured embedding models for grouped data"
    - "enriching word vectors with subword information"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"

Connected through hyperedge HE-4 (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"
    - "the umd neural machine translation systems at wmt17 bandit learning task"
    - "muse modularizing unsupervised sense embeddings"
    - "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-5 (induced by source paper "multi sense embeddings from topic models"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "making sense of word embeddings"
    - "de conflated semantic representations"
    - "embedding words and senses together via joint knowledge enhanced training"
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "autoextend extending word embeddings to embeddings for synsets and lexemes"
    - "do multi sense embeddings improve natural language understanding"

Connected through hyperedge HE-6 (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-7 (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"
    - "muse modularizing unsupervised sense embeddings"
    - "de conflated semantic representations"

Connected through hyperedge HE-8 (induced by source paper "words can shift dynamically adjusting word representations using nonverbal behaviors"):
  with members:
    - "multimodal language analysis with recurrent multistage fusion"
    - "multi attention recurrent network for human communication comprehension"
    - "multimodal affective analysis using hierarchical attention strategy with word level alignment"
    - "multimodal local global ranking fusion for emotion recognition"
    - "deep contextualized word representations"
    - "seq2seq2sentiment multimodal sequence to sequence models for sentiment analysis"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "probabilistic fasttext for multi sense word embeddings"

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)