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: "xnmt the extensible neural machine translation toolkit"
  Abstract: This paper describes XNMT, the eXtensible Neural Machine Translation toolkit. XNMT distin- guishes itself from other open-source NMT toolkits by its focus on modular code design, with the purpose of enabling fast iteration in research and replicable, reliable results. In this paper we describe the design of XNMT and its experiment configuration system, and demonstrate its utility on the tasks of machine translation, speech recognition, and multi-tasked machine translation/parsing. XNMT is available open-source at this https URL

Connected through hyperedge HE-1 (induced by source paper "on the weaknesses of reinforcement learning for neural machine translation"):
  with members:
    - "dora the explorer directed outreaching reinforcement action selection"
    - "a study of reinforcement learning for neural machine translation"
    - "diversity is all you need learning skills without a reward function"
    - "evaluating text gans as language models"
    - "language gans falling short"
    - "thumt an open source toolkit for neural machine translation"
    - "parameter space noise for exploration"
    - "hindsight experience replay"

Connected through hyperedge HE-2 (induced by source paper "choosing transfer languages for cross lingual learning"):
  with members:
    - "distant supervision from disparate sources for low resource part of speech tagging"
    - "neural factor graph models for cross lingual morphological tagging"
    - "near or far wide range zero shot cross lingual dependency parsing"
    - "zero shot neural transfer for cross lingual entity linking"
    - "on difficulties of cross lingual transfer with order differences a case study on dependency parsing"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "rapid adaptation of neural machine translation to new languages"

Connected through hyperedge HE-3 (induced by source paper "leafnats an open source toolkit and live demo system for neural abstractive text summarization"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "deep reinforcement learning for sequence to sequence models"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "texar a modularized versatile and extensible toolkit for text generation"
    - "neural abstractive text summarization with sequence to sequence models a survey"
    - "bottom up abstractive summarization"
    - "opennmt open source toolkit for neural machine translation"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-4 (induced by source paper "exploring phoneme level speech representations for end to end speech translation"):
  with members:
    - "revisiting character based neural machine translation with capacity and compression"
    - "optimizing segmentation granularity for neural machine translation"
    - "tied multitask learning for neural speech translation"
    - "low resource speech to text translation"
    - "pre training on high resource speech recognition improves low resource speech to text translation"
    - "domain robust feature extraction for rapid low resource asr development"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "self attentional acoustic models"

Connected through hyperedge HE-5 (induced by source paper "few shot and zero shot learning for historical text normalization"):
  with members:
    - "a large scale comparison of historical text normalization systems"
    - "tied multitask learning for neural speech translation"
    - "an evaluation of neural machine translation models on historical spelling normalization"
    - "evaluating historical text normalization systems how well do they generalize"
    - "unimorph 2 0 universal morphology"
    - "scheduled multi task learning from syntax to translation"
    - "one shot neural cross lingual transfer for paradigm completion"
    - "identifying beneficial task relations for multi task learning in deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "fluent translations from disfluent speech in end to end speech translation"):
  with members:
    - "low resource speech to text translation"
    - "towards fluent translations from disfluent speech"
    - "self attentional acoustic models"
    - "sequence to sequence models can directly translate foreign speech"
    - "improving lexical choice in neural machine translation"
    - "disfluency detection using a bidirectional lstm"
    - "very deep convolutional networks for end to end speech recognition"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "the ariel cmu systems for lorehlt18"):
  with members:
    - "domain robust feature extraction for rapid low resource asr development"
    - "sequence based multi lingual low resource speech recognition"
    - "enriching word vectors with subword information"
    - "learning crosslingual word embeddings without bilingual corpora"
    - "incorporating discrete translation lexicons into neural machine translation"
    - "bucking the trend large scale cost focused active learning for statistical machine translation"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-8 (induced by source paper "joey nmt a minimalist nmt toolkit for novices"):
  with members:
    - "opennmt neural machine translation toolkit"
    - "a call for clarity in reporting bleu scores"
    - "attention is all you need"
    - "sequence to sequence learning as beam search optimization"
    - "an actor critic algorithm for sequence prediction"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation by jointly learning to align and translate"

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)