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: "composing distributed representations of relational patterns"
  Abstract: Learning distributed representations for relation instances is a central technique in downstream NLP applications. In order to address semantic modeling of relational patterns, this paper constructs a new dataset that provides multiple similarity ratings for every pair of relational patterns on the existing dataset. In addition, we conduct a comparative study of different encoders including additive composition, RNN, LSTM, and GRU for composing distributed representations of relational patterns. We also present Gated Additive Composition, which is an enhancement of additive composition with the gating mechanism. Experiments show that the new dataset does not only enable detailed analyses of the different encoders, but also provides a gauge to predict successes of distributed representations of relational patterns in the relation classification task.

Connected through hyperedge HE-1 (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"
    - "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"
    - "the mechanism of additive composition"
    - "dynamic evaluation of neural sequence models"

Connected through hyperedge HE-2 (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"
    - "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"
    - "the mechanism of additive composition"
    - "revisiting recurrent networks for paraphrastic sentence embeddings"
    - "discriminative phrase embedding for paraphrase identification"

Connected through hyperedge HE-3 (induced by source paper "the mechanism of additive composition"):
  with members:
    - "learning semantically and additively compositional distributional representations"
    - "a latent variable model approach to pmi based word embeddings"
    - "autoextend extending word embeddings to embeddings for synsets and lexemes"
    - "word emdeddings through hellinger pca"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"
    - "a context theoretic framework for compositionality in distributional semantics"
    - "experimental support for a categorical compositional distributional model of meaning"

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)