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: "vocabulary selection strategies for neural machine translation"
  Abstract: Classical translation models constrain the space of possible outputs by selecting a subset of translation rules based on the input sentence. Recent work on improving the efficiency of neural translation models adopted a similar strategy by restricting the output vocabulary to a subset of likely candidates given the source. In this paper we experiment with context and embedding-based selection methods and extend previous work by examining speed and accuracy trade-offs in more detail. We show that decoding time on CPUs can be reduced by up to 90% and training time by 25% on the WMT15 English-German and WMT16 English-Romanian tasks at the same or only negligible change in accuracy. This brings the time to decode with a state of the art neural translation system to just over 140 words per seconds on a single CPU core for English-German.

Connected through hyperedge HE-1 (induced by source paper "hscjn a holistic semantic constraint joint network for diverse response generation"):
  with members:
    - "entropy enhanced multimodal attention model for scene aware dialogue generation"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "dp gan diversity promoting generative adversarial network for generating informative and diversified text"
    - "a knowledge grounded neural conversation model"
    - "dailydialog a manually labelled multi turn dialogue dataset"
    - "neural machine translation with word predictions"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "a simple fast diverse decoding algorithm for neural generation"

Connected through hyperedge HE-2 (induced by source paper "grammatical sequence prediction for real time neural semantic parsing"):
  with members:
    - "symbolic priors for rnn based semantic parsing"
    - "tranx a transition based neural abstract syntax parser for semantic parsing and code generation"
    - "coarse to fine decoding for neural semantic parsing"
    - "a syntactic neural model for general purpose code generation"
    - "generalization without systematicity on the compositional skills of sequence to sequence recurrent networks"
    - "language to logical form with neural attention"
    - "neural symbolic machines learning semantic parsers on freebase with weak supervision"
    - "log linear rnns towards recurrent neural networks with flexible prior knowledge"

Connected through hyperedge HE-3 (induced by source paper "cross lingual transfer learning for multilingual task oriented dialog"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "polyglot contextual representations improve crosslingual transfer"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "pytext a seamless path from nlp research to production"
    - "on the strength of character language models for multilingual named entity recognition"
    - "parameter sharing between dependency parsers for related languages"
    - "zero shot cross lingual classification using multilingual neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "open vocabulary learning for neural chinese pinyin ime"):
  with members:
    - "dependency or span end to end uniform semantic role labeling"
    - "lattice based transformer encoder for neural machine translation"
    - "head driven phrase structure grammar parsing on penn treebank"
    - "effective character augmented word embedding for machine reading comprehension"
    - "subword augmented embedding for cloze reading comprehension"
    - "one shot learning for question answering in gaokao history challenge"
    - "dynamic sentence sampling for efficient training of neural machine translation"
    - "lingke a fine grained multi turn chatbot for customer service"

Connected through hyperedge HE-5 (induced by source paper "neural based pinyin to character conversion with adaptive vocabulary"):
  with members:
    - "dependency or span end to end uniform semantic role labeling"
    - "lattice based transformer encoder for neural machine translation"
    - "head driven phrase structure grammar parsing on penn treebank"
    - "effective character augmented word embedding for machine reading comprehension"
    - "subword augmented embedding for cloze reading comprehension"
    - "modeling multi turn conversation with deep utterance aggregation"
    - "character word lstm language models"
    - "fast and accurate neural word segmentation for chinese"

Connected through hyperedge HE-6 (induced by source paper "learning to compute word embeddings on the fly"):
  with members:
    - "dynamic integration of background knowledge in neural nlu systems"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "a comparative study of word embeddings for reading comprehension"
    - "leveraging lexical resources for learning entity embeddings in multi relational data"
    - "enhancing and combining sequential and tree lstm for natural language inference"
    - "squad 100 000 questions for machine comprehension of text"
    - "vocabulary manipulation for neural machine translation"
    - "dynamic coattention networks for question answering"

Connected through hyperedge HE-7 (induced by source paper "convolutional sequence to sequence learning"):
  with members:
    - "cutting off redundant repeating generations for neural abstractive summarization"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "neural headline generation with minimum risk training"
    - "conditional image generation with pixelcnn decoders"
    - "vocabulary manipulation for neural machine translation"
    - "language modeling with gated convolutional networks"
    - "deep recurrent models with fast forward connections for neural machine translation"
    - "quasi recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "neural response generation with dynamic vocabularies"):
  with members:
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "generating high quality and informative conversation responses with sequence to sequence models"
    - "adversarial learning for neural dialogue generation"
    - "neural machine translation with word predictions"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "a persona based neural conversation model"
    - "deep reinforcement learning for dialogue generation"
    - "sequence to backward and forward sequences a content introducing approach to generative short text conversation"

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)