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: "specialising word vectors for lexical entailment"
  Abstract: We present LEAR (Lexical Entailment Attract-Repel), a novel post-processing method that transforms any input word vector space to emphasise the asymmetric relation of lexical entailment (LE), also known as the is-a or hyponymy-hypernymy relation. By injecting external linguistic constraints (e.g., WordNet links) into the initial vector space, the LE specialisation procedure brings true hyponymy-hypernymy pairs closer together in the transformed Euclidean space. The proposed asymmetric distance measure adjusts the norms of word vectors to reflect the actual WordNet-style hierarchy of concepts. Simultaneously, a joint objective enforces semantic similarity using the symmetric cosine distance, yielding a vector space specialised for both lexical relations at once. LEAR specialisation achieves state-of-the-art performance in the tasks of hypernymy directionality, hypernymy detection and graded lexical entailment, demonstrating the effectiveness and robustness of the proposed model.

Connected through hyperedge HE-1 (induced by source paper "inducing relational knowledge from bert"):
  with members:
    - "analogies explained towards understanding word embeddings"
    - "assessing bert s syntactic abilities"
    - "bert rediscovers the classical nlp pipeline"
    - "relational word embeddings"
    - "barack s wife hillary using knowledge graphs for fact aware language modeling"
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "fair is better than sensational man is to doctor as woman is to doctor"
    - "do neural language representations learn physical commonsense"

Connected through hyperedge HE-2 (induced by source paper "relational word embeddings"):
  with members:
    - "integrating multiplicative features into supervised distributional methods for lexical entailment"
    - "pair2vec compositional word pair embeddings for cross sentence inference"
    - "filling missing paths modeling co occurrences of word pairs and dependency paths for recognizing lexical semantic relations"
    - "neural latent relational analysis to capture lexical semantic relations in a vector space"
    - "hierarchical embeddings for hypernymy detection and directionality"
    - "hyperlex a large scale evaluation of graded lexical entailment"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-3 (induced by source paper "antonym synonym classification based on new sub space embeddings"):
  with members:
    - "one shot and few shot learning of word embeddings"
    - "deep contextualized word representations"
    - "distinguishing antonyms and synonyms in a pattern based neural network"
    - "fasttext zip compressing text classification models"
    - "poincar e embeddings for learning hierarchical representations"
    - "xgboost a scalable tree boosting system"
    - "adam a method for stochastic optimization"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-4 (induced by source paper "embedding text in hyperbolic spaces"):
  with members:
    - "trimming and improving skip thought vectors"
    - "hierarchical embeddings for hypernymy detection and directionality"
    - "discourse based objectives for fast unsupervised sentence representation learning"
    - "dissent sentence representation learning from explicit discourse relations"
    - "unsupervised hypernym detection by distributional inclusion vector embedding"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "speeding up context based sentence representation learning with non autoregressive convolutional decoding"
    - "hyperlex a large scale evaluation of graded lexical entailment"

Connected through hyperedge HE-5 (induced by source paper "awe asymmetric word embedding for textual entailment"):
  with members:
    - "end task oriented textual entailment via deep explorations of inter sentence interactions"
    - "semantic sentence matching with densely connected recurrent and co attentive information"
    - "natural language inference over interaction space"
    - "hierarchical embeddings for hypernymy detection and directionality"
    - "attention is all you need"
    - "learned in translation contextualized word vectors"
    - "bilateral multi perspective matching for natural language sentences"
    - "a decomposable attention model for natural language inference"

Connected through hyperedge HE-6 (induced by source paper "seven augmenting word embeddings with unsupervised relation vectors"):
  with members:
    - "comparative study of cnn and rnn for natural language processing"
    - "modeling semantic relatedness using global relation vectors"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "embedding words and senses together via joint knowledge enhanced training"
    - "efficient character level document classification by combining convolution and recurrent layers"
    - "take and took gaggle and goose book and read evaluating the utility of vector differences for lexical relation learning"
    - "classifying relations via long short term memory networks along shortest dependency path"

Connected through hyperedge HE-7 (induced by source paper "adversarial propagation and zero shot cross lingual transfer of word vector specialization"):
  with members:
    - "modeling language variation and universals a survey on typological linguistics for natural language processing"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "cross lingual models of word embeddings an empirical comparison"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "improved techniques for training gans"
    - "generative adversarial text to image synthesis"
    - "adversarial deep averaging networks for cross lingual sentiment classification"
    - "neural belief tracker data driven dialogue state tracking"

Connected through hyperedge HE-8 (induced by source paper "post specialisation retrofitting vectors of words unseen in lexical resources"):
  with members:
    - "self normalizing neural networks"
    - "sigmoid weighted linear units for neural network function approximation in reinforcement learning"
    - "word translation without parallel data"
    - "cross lingual induction and transfer of verb classes based on word vector space specialisation"
    - "continuously learning neural dialogue management"
    - "neural belief tracker data driven dialogue state tracking"
    - "separated by an un common language towards judgment language informed vector space modeling"
    - "adam a method for stochastic optimization"

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)