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: "zero shot question generation from knowledge graphs for unseen predicates and entity types"
  Abstract: We present a neural model for question generation from knowledge base triples in a "Zero-Shot" setup, that is generating questions for triples containing predicates, subject types or object types that were not seen at training time. Our model leverages triples occurrences in the natural language corpus in an encoder-decoder architecture, paired with an original part-of-speech copy action mechanism to generate questions. Benchmark and human evaluation show that our model sets a new state-of-the-art for zero-shot QG.

Connected through hyperedge HE-1 (induced by source paper "reinforcement learning based graph to sequence model for natural question generation"):
  with members:
    - "bidirectional attentive memory networks for question answering over knowledge bases"
    - "learning to generate questions by learningwhat not to generate"
    - "graphflow exploiting conversation flow with graph neural networks for conversational machine comprehension"
    - "reinforcement learning based text style transfer without parallel training corpus"
    - "deep iterative and adaptive learning for graph neural networks"
    - "unified language model pre training for natural language understanding and generation"
    - "sql to text generation with graph to sequence model"
    - "towards a better metric for evaluating question generation systems"

Connected through hyperedge HE-2 (induced by source paper "recent advances in neural question generation"):
  with members:
    - "question generation from sql queries improves neural semantic parsing"
    - "towards a better metric for evaluating question generation systems"
    - "improving neural question generation using answer separation"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "automating reading comprehension by generating question and answer pairs"
    - "harvesting paragraph level question answer pairs from wikipedia"
    - "neural generation of diverse questions using answer focus contextual and linguistic features"
    - "neural models for key phrase detection and question generation"

Connected through hyperedge HE-3 (induced by source paper "keeping notes conditional natural language generation with a scratchpad mechanism"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "bottom up abstractive summarization"
    - "neural argument generation augmented with externally retrieved evidence"
    - "learning to ask neural question generation for reading comprehension"
    - "neural text generation a practical guide"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "table to text natural language generation with unseen schemas"):
  with members:
    - "key fact as pivot a two stage model for low resource table to text generation"
    - "domain adaptive dialog generation via meta learning"
    - "domain adaptation of neural machine translation by lexicon induction"
    - "step by step separating planning from realization in neural data to text generation"
    - "learning to compose topic aware mixture of experts for zero shot video captioning"
    - "natural language to structured query generation via meta learning"
    - "data to text generation with content selection and planning"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-5 (induced by source paper "easy to hard leveraging simple questions for complex question generation"):
  with members:
    - "multimodal differential network for visual question generation"
    - "learning to ask questions in open domain conversational systems with typed decoders"
    - "the web as a knowledge base for answering complex questions"
    - "toward abstractive summarization using semantic representations"
    - "learning to ask neural question generation for reading comprehension"
    - "get to the point summarization with pointer generator networks"
    - "creativity generating diverse questions using variational autoencoders"
    - "question answering and question generation as dual tasks"

Connected through hyperedge HE-6 (induced by source paper "learning representation mapping for relation detection in knowledge base question answering"):
  with members:
    - "simplequestions nearly solved a new upperbound and baseline approach"
    - "a teacher student framework for zero resource neural machine translation"
    - "improved neural relation detection for knowledge base question answering"
    - "zero shot relation extraction via reading comprehension"
    - "word translation without parallel data"
    - "zero resource translation with multi lingual neural machine translation"
    - "toward multilingual neural machine translation with universal encoder and decoder"
    - "simple question answering by attentive convolutional neural network"

Connected through hyperedge HE-7 (induced by source paper "generating questions for knowledge bases via incorporating diversified contexts and answer aware loss"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "simplequestions nearly solved a new upperbound and baseline approach"
    - "attention is all you need"
    - "strong baselines for simple question answering over knowledge graphs with and without neural networks"
    - "ruminating reader reasoning with gated multi hop attention"
    - "disan directional self attention network for rnn cnn free language understanding"
    - "generating factoid questions with recurrent neural networks the 30m factoid question answer corpus"
    - "incorporating copying mechanism in sequence to sequence learning"

Connected through hyperedge HE-8 (induced by source paper "zero shot dialog generation with cross domain latent actions"):
  with members:
    - "key value retrieval networks for task oriented dialogue"
    - "towards zero shot frame semantic parsing for domain scaling"
    - "zero shot task generalization with multi task deep reinforcement learning"
    - "bootstrapping incremental dialogue systems from minimal data the generalisation power of dialogue grammars"
    - "a copy augmented sequence to sequence architecture gives good performance on task oriented dialogue"
    - "generative encoder decoder models for task oriented spoken dialog systems with chatting capability"
    - "one shot imitation learning"
    - "a network based end to end trainable task oriented dialogue system"

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)