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: "de conflated semantic representations"
  Abstract: One major deficiency of most semantic representation techniques is that they usually model a word type as a single point in the semantic space, hence conflating all the meanings that the word can have. Addressing this issue by learning distinct representations for individual meanings of words has been the subject of several research studies in the past few years. However, the generated sense representations are either not linked to any sense inventory or are unreliable for infrequent word senses. We propose a technique that tackles these problems by de-conflating the representations of words based on the deep knowledge it derives from a semantic network. Our approach provides multiple advantages in comparison to the past work, including its high coverage and the ability to generate accurate representations even for infrequent word senses. We carry out evaluations on six datasets across two semantic similarity tasks and report state-of-the-art results on most of them.

Connected through hyperedge HE-1 (induced by source paper "using multi sense vector embeddings for reverse dictionaries"):
  with members:
    - "one single deep bidirectional lstm network for word sense disambiguation of text data"
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "deep contextualized word representations"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "learning sentence embeddings using recursive networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "mapping text to knowledge graph entities using multi sense lstms"

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"
    - "a mixture model for learning multi sense word embeddings"
    - "learning to explain non standard english words and phrases"
    - "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 "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"
    - "muse modularizing unsupervised sense embeddings"
    - "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-4 (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"
    - "a mixture model for learning multi sense word embeddings"
    - "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-5 (induced by source paper "wic the word in context dataset for evaluating context sensitive meaning representations"):
  with members:
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "towards a seamless integration of word senses into downstream nlp applications"
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "word usage similarity estimation with sentence representations and automatic substitutes"):
  with members:
    - "deep contextualized word representations"
    - "wic 10 000 example pairs for evaluating context sensitive representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "an empirical evaluation of doc2vec with practical insights into document embedding generation"
    - "distributed representations of sentences and documents"
    - "efficient estimation of word representations in vector space"

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"
    - "a mixture model for learning multi sense word embeddings"
    - "muse modularizing unsupervised sense embeddings"

Connected through hyperedge HE-8 (induced by source paper "learning graph embeddings from wordnet based similarity measures"):
  with members:
    - "a constrained randomized shortest paths framework for optimal exploration"
    - "representation learning on graphs methods and applications"
    - "semantic specialisation of distributional word vector spaces using monolingual and cross lingual constraints"
    - "node2vec scalable feature learning for networks"
    - "autoextend extending word embeddings to embeddings for synsets and lexemes"
    - "adam a method for stochastic optimization"
    - "deepwalk online learning of social representations"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"

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)