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: "knowledge based word sense disambiguation using topic models"
  Abstract: Word Sense Disambiguation is an open problem in Natural Language Processing which is particularly challenging and useful in the unsupervised setting where all the words in any given text need to be disambiguated without using any labeled data. Typically WSD systems use the sentence or a small window of words around the target word as the context for disambiguation because their computational complexity scales exponentially with the size of the context. In this paper, we leverage the formalism of topic model to design a WSD system that scales linearly with the number of words in the context. As a result, our system is able to utilize the whole document as the context for a word to be disambiguated. The proposed method is a variant of Latent Dirichlet Allocation in which the topic proportions for a document are replaced by synset proportions. We further utilize the information in the WordNet by assigning a non-uniform prior to synset distribution over words and a logistic-normal prior for document distribution over synsets. We evaluate the proposed method on Senseval-2, Senseval-3, SemEval-2007, SemEval-2013 and SemEval-2015 English All-Word WSD datasets and show that it outperforms the state-of-the-art unsupervised knowledge-based WSD system by a significant margin.

Connected through hyperedge HE-1 (induced by source paper "semantic relatedness for keyword disambiguation exploiting different embeddings"):
  with members:
    - "word sense disambiguation a complex network approach"
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "deep contextualized word representations"
    - "embedding words and senses together via joint knowledge enhanced training"
    - "an empirical evaluation of doc2vec with practical insights into document embedding generation"
    - "distributed representations of sentences and documents"
    - "distributed representations of words and phrases and their compositionality"
    - "distributional measures of semantic distance a survey"

Connected through hyperedge HE-2 (induced by source paper "zero shot entity linking by reading entity descriptions"):
  with members:
    - "cross domain regularization for neural ranking models using adversarial learning"
    - "multi source domain adaptation with mixture of experts"
    - "improving entity linking by modeling latent relations between mentions"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "hierarchical losses and new resources for fine grained entity typing and linking"

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"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "a survey of word embeddings evaluation methods"

Connected through hyperedge HE-4 (induced by source paper "a short survey on sense annotated corpora for diverse languages and resources"):
  with members:
    - "huge automatically extracted training sets for multilingual word sense disambiguation"
    - "shotgunwsd an unsupervised algorithm for global word sense disambiguation inspired by dna sequencing"
    - "named entity disambiguation for noisy text"
    - "semi supervised word sense disambiguation with neural models"
    - "word sense disambiguation using a bidirectional lstm"

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)