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 game theoretic approach to word sense disambiguation"
  Abstract: This article presents a new model for word sense disambiguation formulated in terms of evolutionary game theory, where each word to be disambiguated is represented as a node on a graph whose edges represent word relations and senses are represented as classes. The words simultaneously update their class membership preferences according to the senses that neighboring words are likely to choose. We use distributional information to weigh the influence that each word has on the decisions of the others and semantic similarity information to measure the strength of compatibility among the choices. With this information we can formulate the word sense disambiguation problem as a constraint satisfaction problem and solve it using tools derived from game theory, maintaining the textual coherence. The model is based on two ideas: Similar words should be assigned to similar classes and the meaning of a word does not depend on all the words in a text but just on some of them. The article provides an in-depth motivation of the idea of modeling the word sense disambiguation problem in terms of game theory, which is illustrated by an example. The conclusion presents an extensive analysis on the combination of similarity measures to use in the framework and a comparison with state-of-the-art systems. The results show that our model outperforms state-of-the-art algorithms and can be applied to different tasks and in different scenarios.

Connected through hyperedge HE-1 (induced by source paper "word sense disambiguation using knowledge based word similarity"):
  with members:
    - "deep contextualized word representations"
    - "deep reinforcement learning an overview"
    - "when is multitask learning effective semantic sequence prediction under varying data conditions"
    - "distributed representations of sentences and documents"

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 "a novel neural sequence model with multiple attentions for word sense disambiguation"):
  with members:
    - "one single deep bidirectional lstm network for word sense disambiguation of text data"
    - "shotgunwsd an unsupervised algorithm for global word sense disambiguation inspired by dna sequencing"
    - "semi supervised word sense disambiguation with neural models"
    - "word sense disambiguation using a bidirectional lstm"
    - "linguistic input features improve neural machine translation"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-4 (induced by source paper "kdsl a knowledge driven supervised learning framework for word sense disambiguation"):
  with members:
    - "word sense disambiguation with lstm do we really need 100 billion words"
    - "semi supervised word sense disambiguation with neural models"
    - "word sense disambiguation using a bidirectional lstm"
    - "a large scale multilingual disambiguation of glosses"
    - "autoextend extending word embeddings to embeddings for synsets and lexemes"
    - "on using very large target vocabulary for neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "word sense disambiguation with lstm do we really need 100 billion words"):
  with members:
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "transition based dependency parsing with stack long short term memory"
    - "autoextend extending word embeddings to embeddings for synsets and lexemes"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-6 (induced by source paper "context aware nonnegative matrix factorization clustering"):
  with members:
    - "document clustering games in static and dynamic scenarios"
    - "a tutorial on spectral clustering"

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.AI(Artificial Intelligence)