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: "sequence to backward and forward sequences a content introducing approach to generative short text conversation"
  Abstract: Using neural networks to generate replies in human-computer dialogue systems is attracting increasing attention over the past few years. However, the performance is not satisfactory: the neural network tends to generate safe, universally relevant replies which carry little meaning. In this paper, we propose a content-introducing approach to neural network-based generative dialogue systems. We first use pointwise mutual information (PMI) to predict a noun as a keyword, reflecting the main gist of the reply. We then propose seq2BF, a "sequence to backward and forward sequences" model, which generates a reply containing the given keyword. Experimental results show that our approach significantly outperforms traditional sequence-to-sequence models in terms of human evaluation and the entropy measure, and that the predicted keyword can appear at an appropriate position in the reply.

Connected through hyperedge HE-1 (induced by source paper "non autoregressive neural dialogue generation"):
  with members:
    - "avgout a simple output probability measure to eliminate dull responses"
    - "towards a human like open domain chatbot"
    - "latent variable non autoregressive neural machine translation with deterministic inference using a delta posterior"
    - "comparison of diverse decoding methods from conditional language models"
    - "fast structured decoding for sequence models"
    - "complexity weighted loss and diverse reranking for sentence simplification"
    - "pnat non autoregressive transformer by position learning"
    - "dialogpt large scale generative pre training for conversational response generation"

Connected through hyperedge HE-2 (induced by source paper "posterior gan towards informative and coherent response generation with posterior generative adversarial network"):
  with members:
    - "improving neural conversational models with entropy based data filtering"
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "better conversations by modeling filtering and optimizing for coherence and diversity"
    - "a study of reinforcement learning for neural machine translation"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "improving variational encoder decoders in dialogue generation"
    - "dailydialog a manually labelled multi turn dialogue dataset"
    - "latent variable dialogue models and their diversity"

Connected through hyperedge HE-3 (induced by source paper "generating emotionally aligned responses in dialogues using affect control theory"):
  with members:
    - "disentangled representation learning for non parallel text style transfer"
    - "the design and implementation of xiaoice an empathetic social chatbot"
    - "emma an emotionally intelligent personal assistant for improving wellbeing"
    - "finding good representations of emotions for text classification"
    - "sentiment adaptive end to end dialog systems"
    - "toward controlled generation of text"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "mojitalk generating emotional responses at scale"

Connected through hyperedge HE-4 (induced by source paper "improving neural conversational models with entropy based data filtering"):
  with members:
    - "deep learning based chatbot models"
    - "jointly optimizing diversity and relevance in neural response generation"
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "importance of search and evaluation strategies in neural dialogue modeling"
    - "neural response ranking for social conversation a data efficient approach"
    - "automatic evaluation of neural personality based chatbots"
    - "better conversations by modeling filtering and optimizing for coherence and diversity"
    - "towards explainable and controllable open domain dialogue generation with dialogue acts"

Connected through hyperedge HE-5 (induced by source paper "a survey of natural language generation techniques with a focus on dialogue systems past present and future directions"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "personalized dialogue generation with diversified traits"
    - "learning to write with cooperative discriminators"
    - "training millions of personalized dialogue agents"
    - "towards empathetic open domain conversation models a new benchmark and dataset"
    - "wizard of wikipedia knowledge powered conversational agents"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "polite dialogue generation without parallel data"

Connected through hyperedge HE-6 (induced by source paper "relevance promoting language model for short text conversation"):
  with members:
    - "generating multiple diverse responses with multi mapping and posterior mapping selection"
    - "dlgnet a transformer based model for dialogue response generation"
    - "the curious case of neural text degeneration"
    - "comparison of diverse decoding methods from conditional language models"
    - "transformer xl attentive language models beyond a fixed length context"
    - "a discrete cvae for response generation on short text conversation"
    - "hello it s gpt 2 how can i help you towards the use of pretrained language models for task oriented dialogue systems"
    - "dialogpt large scale generative pre training for conversational response generation"

Connected through hyperedge HE-7 (induced by source paper "diversifying topic coherent response generation for natural multi turn conversations"):
  with members:
    - "learning to ask questions in open domain conversational systems with typed decoders"
    - "multi level memory for task oriented dialogs"
    - "variational memory encoder decoder"
    - "language gans falling short"
    - "generating more interesting responses in neural conversation models with distributional constraints"
    - "diverse beam search for increased novelty in abstractive summarization"
    - "toward controlled generation of text"
    - "dailydialog a manually labelled multi turn dialogue dataset"

Connected through hyperedge HE-8 (induced by source paper "araml a stable adversarial training framework for text generation"):
  with members:
    - "incorporating discriminator in sentence generation a gibbs sampling method"
    - "on accurate evaluation of gans for language generation"
    - "towards diverse text generation with inverse reinforcement learning"
    - "texygen a benchmarking platform for text generation models"
    - "cgmh constrained sentence generation by metropolis hastings sampling"
    - "language gans falling short"
    - "the price of debiasing automatic metrics in natural language evaluation"
    - "emotional chatting machine emotional conversation generation with internal 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)