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: "learning to understand phrases by embedding the dictionary"
  Abstract: Distributional models that learn rich semantic word representations are a success story of recent NLP research. However, developing models that learn useful representations of phrases and sentences has proved far harder. We propose using the definitions found in everyday dictionaries as a means of bridging this gap between lexical and phrasal semantics. Neural language embedding models can be effectively trained to map dictionary definitions (phrases) to (lexical) representations of the words defined by those definitions. We present two applications of these architectures: "reverse dictionaries" that return the name of a concept given a definition or description and general-knowledge crossword question answerers. On both tasks, neural language embedding models trained on definitions from a handful of freely-available lexical resources perform as well or better than existing commercial systems that rely on significant task-specific engineering. The results highlight the effectiveness of both neural embedding architectures and definition-based training for developing models that understand phrases and sentences.

Connected through hyperedge HE-1 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity"):
  with members:
    - "multilingual alignment of contextual word representations"
    - "emerging cross lingual structure in pretrained language models"
    - "an empirical study on post processing methods for word embeddings"
    - "informing unsupervised pretraining with external linguistic knowledge"
    - "on the robustness of unsupervised and semi supervised cross lingual word embedding learning"
    - "word similarity datasets for thai construction and evaluation"
    - "paws paraphrase adversaries from word scrambling"
    - "cross lingual ability of multilingual bert an empirical study"

Connected through hyperedge HE-2 (induced by source paper "lexical sememe prediction using dictionary definitions by capturing local semantic correspondence"):
  with members:
    - "incorporating glosses into neural word sense disambiguation"
    - "incorporating chinese characters of words for lexical sememe prediction"
    - "language modeling with sparse product of sememe experts"
    - "sememe prediction learning semantic knowledge from unstructured textual wiki descriptions"
    - "implementing a reverse dictionary based on word definitions using a node graph architecture"
    - "neural machine translation of rare words with subword units"
    - "adam a method for stochastic optimization"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "combining pretrained high resource embeddings and subword representations for low resource languages"):
  with members:
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "learning word vectors for 157 languages"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "opennmt open source toolkit for neural machine translation"
    - "enriching word vectors with subword information"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "improving interpretability of word embeddings by generating definition and usage"):
  with members:
    - "incorporating sememes into chinese definition modeling"
    - "xsense learning sense separated sparse representations and textual definitions for explainable word sense networks"
    - "deep contextualized word representations"
    - "semi supervised sequence modeling with cross view training"
    - "conditional generators of words definitions"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"
    - "exploiting linguistic resources for neural machine translation using multi task learning"

Connected through hyperedge HE-5 (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-6 (induced by source paper "sparsity emerges naturally in neural language models"):
  with members:
    - "a deep generative model of vowel formant typology"
    - "what is one grain of sand in the desert analyzing individual neurons in deep nlp models"
    - "understanding learning dynamics of language models with svcca"
    - "sharp nearby fuzzy far away how neural language models use context"
    - "learning to generate reviews and discovering sentiment"
    - "exploring sparsity in recurrent neural networks"
    - "opening the black box of deep neural networks via information"
    - "spine sparse interpretable neural embeddings"

Connected through hyperedge HE-7 (induced by source paper "learning to decipher hate symbols"):
  with members:
    - "generating sentences by editing prototypes"
    - "recognizing explicit and implicit hate speech using a weakly supervised two path bootstrapping approach"
    - "learning to explain non standard english words and phrases"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "definition modeling learning to define word embeddings in natural language"
    - "ex machina personal attacks seen at scale"
    - "variational neural machine translation"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-8 (induced by source paper "word embeddings for entity annotated texts"):
  with members:
    - "verse versatile graph embeddings from similarity measures"
    - "efficient graph computation for node2vec"
    - "graph embedding techniques applications and performance a survey"
    - "a survey of word embeddings evaluation methods"
    - "query expansion with locally trained word embeddings"
    - "node2vec scalable feature learning for networks"
    - "enriching word vectors with subword information"
    - "joint learning of the embedding of words and entities for named entity disambiguation"

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)