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: "a survey of natural language generation techniques with a focus on dialogue systems past present and future directions"
  Abstract: One of the hardest problems in the area of Natural Language Processing and Artificial Intelligence is automatically generating language that is coherent and understandable to humans. Teaching machines how to converse as humans do falls under the broad umbrella of Natural Language Generation. Recent years have seen unprecedented growth in the number of research articles published on this subject in conferences and journals both by academic and industry researchers. There have also been several workshops organized alongside top-tier NLP conferences dedicated specifically to this problem. All this activity makes it hard to clearly define the state of the field and reason about its future directions. In this work, we provide an overview of this important and thriving area, covering traditional approaches, statistical approaches and also approaches that use deep neural networks. We provide a comprehensive review towards building open domain dialogue systems, an important application of natural language generation. We find that, predominantly, the approaches for building dialogue systems use seq2seq or language models architecture. Notably, we identify three important areas of further research towards building more effective dialogue systems: 1) incorporating larger context, including conversation context and world knowledge; 2) adding personae or personality in the NLG system; and 3) overcoming dull and generic responses that affect the quality of system-produced responses. We provide pointers on how to tackle these open problems through the use of cognitive architectures that mimic human language understanding and generation capabilities.

Connected through hyperedge HE-1 (induced by source paper "introducing aspects of creativity in automatic poetry generation"):
  with members:
    - "bertscore evaluating text generation with bert"
    - "deep speare a joint neural model of poetic language meter and rhyme"
    - "generating lyrics with variational autoencoder and multi modal artist embeddings"
    - "beyond narrative description generating poetry from images by multi adversarial training"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "attention is all you need"
    - "deep reinforcement learning for dialogue generation"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-2 (induced by source paper "detecting asks in se attacks impact of linguistic and structural knowledge"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "investigating evaluation of open domain dialogue systems with human generated multiple references"
    - "allennlp a deep semantic natural language processing platform"
    - "neural approaches to conversational ai"
    - "augmenting neural response generation with context aware topical attention"

Connected through hyperedge HE-3 (induced by source paper "towards best experiment design for evaluating dialogue system output"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "evaluating coherence in dialogue systems using entailment"
    - "rankme reliable human ratings for natural language generation"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "augmenting neural response generation with context aware topical attention"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "hierarchical recurrent attention network for response generation"
    - "towards an automatic turing test learning to evaluate dialogue responses"

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)