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: "the mechanism of additive composition"
  Abstract: Additive composition (Foltz et al. in Discourse Process 15:285---307, 1998; Landauer and Dumais in Psychol Rev 104(2):211, 1997; Mitchell and Lapata in Cognit Sci 34(8):1388---1429, 2010) is a widely used method for computing meanings of phrases, which takes the average of vector representations of the constituent words. In this article, we prove an upper bound for the bias of additive composition, which is the first theoretical analysis on compositional frameworks from a machine learning point of view. The bound is written in terms of collocation strength; we prove that the more exclusively two successive words tend to occur together, the more accurate one can guarantee their additive composition as an approximation to the natural phrase vector. Our proof relies on properties of natural language data that are empirically verified, and can be theoretically derived from an assumption that the data is generated from a Hierarchical Pitman---Yor Process. The theory endorses additive composition as a reasonable operation for calculating meanings of phrases, and suggests ways to improve additive compositionality, including: transforming entries of distributional word vectors by a function that meets a specific condition, constructing a novel type of vector representations to make additive composition sensitive to word order, and utilizing singular value decomposition to train word vectors.

Connected through hyperedge HE-1 (induced by source paper "binarized pmi matrix bridging word embeddings and hyperbolic spaces"):
  with members:
    - "analogies explained towards understanding word embeddings"
    - "visualizing and measuring the geometry of bert"
    - "low rank approximation of matrices for pmi based word embeddings"
    - "poincar e glove hyperbolic word embeddings"
    - "towards understanding linear word analogies"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "what the vec towards probabilistically grounded embeddings"

Connected through hyperedge HE-2 (induced by source paper "character n gram embeddings to improve rnn language models"):
  with members:
    - "an analysis of neural language modeling at multiple scales"
    - "selective encoding for abstractive sentence summarization"
    - "composing distributed representations of relational patterns"
    - "input to output gate to improve rnn language models"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "on the state of the art of evaluation in neural language models"
    - "regularizing and optimizing lstm language models"
    - "dynamic evaluation of neural sequence models"

Connected through hyperedge HE-3 (induced by source paper "temporal and aspectual entailment"):
  with members:
    - "collecting diverse natural language inference problems for sentence representation evaluation"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "using j k fold cross validation to reduce variance when tuning nlp models"
    - "one representation per word does it make sense for composition"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"
    - "advances in pre training distributed word representations"

Connected through hyperedge HE-4 (induced by source paper "a structured distributional model of sentence meaning and processing"):
  with members:
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "measuring thematic fit with distributional feature overlap"
    - "question answering with grammatically interpretable representations"
    - "is structure necessary for modeling argument expectations in distributional semantics"
    - "fine grained analysis of sentence embeddings using auxiliary prediction tasks"
    - "representing verbs with rich contexts an evaluation on verb similarity"
    - "learning distributed representations of sentences from unlabelled data"
    - "skip thought vectors"

Connected through hyperedge HE-5 (induced by source paper "unsupervised sentence representations as word information series revisiting tf idf"):
  with members:
    - "learning semantic textual similarity from conversations"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "composing distributed representations of relational patterns"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "compilig at semeval 2017 task 1 cross language plagiarism detection methods for semantic textual similarity"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"
    - "revisiting recurrent networks for paraphrastic sentence embeddings"
    - "discriminative phrase embedding for paraphrase identification"

Connected through hyperedge HE-6 (induced by source paper "one representation per word does it make sense for composition"):
  with members:
    - "linear algebraic structure of word senses with applications to polysemy"
    - "improving sparse word representations with distributional inference for semantic composition"
    - "aligning packed dependency trees a theory of composition for distributional semantics"
    - "from paraphrase database to compositional paraphrase model and back"
    - "learning to understand phrases by embedding the dictionary"
    - "do multi sense embeddings improve natural language understanding"
    - "a study of entanglement in a categorical framework of natural language"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "learning semantically and additively compositional distributional representations"):
  with members:
    - "classifying relations by ranking with convolutional neural networks"
    - "traversing knowledge graphs in vector space"
    - "a study of entanglement in a categorical framework of natural language"
    - "category theoretic quantitative compositional distributional models of natural language semantics"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "a fast and simple algorithm for training neural probabilistic language models"
    - "experimental support for a categorical compositional distributional model of meaning"

Connected through hyperedge HE-8 (induced by source paper "leveraging lexical resources for learning entity embeddings in multi relational data"):
  with members:
    - "autoextend extending word embeddings to embeddings for synsets and lexemes"
    - "towards universal paraphrastic sentence embeddings"
    - "distributed representations of sentences and documents"
    - "incorporating both distributional and relational semantics in word representations"
    - "embedding entities and relations for learning and inference in knowledge bases"
    - "efficient estimation of word representations in vector space"

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)