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: "neobility at semeval 2017 task 1 an attention based sentence similarity model"
  Abstract: This paper describes a neural-network model which performed competitively (top 6) at the SemEval 2017 cross-lingual Semantic Textual Similarity (STS) task. Our system employs an attention-based recurrent neural network model that optimizes the sentence similarity. In this paper, we describe our participation in the multilingual STS task which measures similarity across English, Spanish, and Arabic.

Connected through hyperedge HE-1 (induced by source paper "unsupervised pidgin text generation by pivoting english data and self training"):
  with members:
    - "select and attend towards controllable content selection in text generation"
    - "zero shot paraphrase generation with multilingual language models"
    - "revisiting self training for neural sequence generation"
    - "pidginunmt unsupervised neural machine translation from west african pidgin to english"
    - "unsupervised machine translation using monolingual corpora only"
    - "unsupervised neural machine translation"
    - "neural text generation from structured data with application to the biography domain"

Connected through hyperedge HE-2 (induced by source paper "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"):
  with members:
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "revisiting recurrent networks for paraphrastic sentence embeddings"
    - "matrix factorization using window sampling and negative sampling for improved word representations"
    - "enhancing and combining sequential and tree lstm for natural language inference"
    - "an empirical evaluation of doc2vec with practical insights into document embedding generation"
    - "learning distributed representations of sentences from unlabelled data"
    - "charagram embedding words and sentences via character n grams"
    - "enhancing the lexvec distributed word representation model using positional contexts and external memory"

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)