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: "topic augmented neural response generation with a joint attention mechanism"
  Abstract: We consider incorporating topic information as prior knowledge into the sequence to sequence (Seq2Seq) network structure with attention mechanism for response generation in chatbots. To this end, we propose a topic augmented joint attention based Seq2Seq (TAJA-Seq2Seq) model. In TAJA-Seq2Seq, information from input posts and information from topics related to the posts are simultaneously embedded into vector spaces by a content encoder and a topic encoder respectively. The two kinds of information interact with each other and help calibrate weights of each other in the joint attention mechanism in TAJA2Seq2Seq, and jointly determine the generation of responses in decoding. The model simulates how people behave in conversation and can generate well-focused and informative responses with the help of topic information. Empirical study on large scale human judged generation results show that our model outperforms Seq2Seq with attention on both response quality and diversity.

Connected through hyperedge HE-1 (induced by source paper "c textgen conditional text generation for harmonious human machine interaction"):
  with members:
    - "towards knowledge based personalized product description generation in e commerce"
    - "improving neural response diversity with frequency aware cross entropy loss"
    - "an adversarial approach to high quality sentiment controlled neural dialogue generation"
    - "topic guided variational autoencoders for text generation"
    - "knowledge aware conversation generation with explainable reasoning on augmented graphs"
    - "knowledge aware conversation generation with reasoning on augmented graph"
    - "reinforcement learning based emotional editing constraint conversation generation"
    - "towards an engine for lifelong interactive knowledge learning in human machine conversations"

Connected through hyperedge HE-2 (induced by source paper "deep conversational recommender in travel"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "towards deep conversational recommendations"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"
    - "conversational recommender system"
    - "a knowledge grounded neural conversation model"
    - "latent intention dialogue models"
    - "neural collaborative filtering"

Connected through hyperedge HE-3 (induced by source paper "an adversarial approach to high quality sentiment controlled neural dialogue generation"):
  with members:
    - "toward controlled generation of text"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "mojitalk generating emotional responses at scale"
    - "multi space variational encoder decoders for semi supervised labeled sequence transduction"
    - "adversarial learning for neural dialogue generation"
    - "a conditional variational framework for dialog generation"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "deep reinforcement learning for dialogue generation"

Connected through hyperedge HE-4 (induced by source paper "building a production model for retrieval based chatbots"):
  with members:
    - "diversifying reply suggestions using a matching conditional variational autoencoder"
    - "a hybrid retrieval generation neural conversation model"
    - "neural response ranking for social conversation a data efficient approach"
    - "an efficient framework for learning sentence representations"
    - "modeling multi turn conversation with deep utterance aggregation"
    - "simple recurrent units for highly parallelizable recurrence"
    - "attention is all you need"
    - "a practical approach to dialogue response generation in closed domains"

Connected through hyperedge HE-5 (induced by source paper "writerforcing generating more interesting story endings"):
  with members:
    - "hierarchically structured reinforcement learning for topically coherent visual story generation"
    - "story ending generation with incremental encoding and commonsense knowledge"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "hierarchical neural story generation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "generating more interesting responses in neural conversation models with distributional constraints"
    - "incorporating structured commonsense knowledge in story completion"
    - "toward controlled generation of text"

Connected through hyperedge HE-6 (induced by source paper "exploiting persona information for diverse generation of conversational responses"):
  with members:
    - "deepcopy grounded response generation with hierarchical pointer networks"
    - "learning end to end goal oriented dialog with multiple answers"
    - "from eliza to xiaoice challenges and opportunities with social chatbots"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "why are sequence to sequence models so dull understanding the low diversity problem of chatbots"
    - "assigning personality identity to a chatting machine for coherent conversation generation"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "a persona based neural conversation model"

Connected through hyperedge HE-7 (induced by source paper "neural generation for czech data and baselines"):
  with members:
    - "evaluating the state of the art of end to end natural language generation the e2e nlg challenge"
    - "end to end content and plan selection for data to text generation"
    - "rdf2pt generating brazilian portuguese texts from rdf data"
    - "are all languages equally hard to language model"
    - "predicting target language ccg supertags improves neural machine translation"
    - "modeling target side inflection in neural machine translation"
    - "natural language generation in dialogue using lexicalized and delexicalized data"
    - "incorporating copying mechanism in sequence to sequence learning"

Connected through hyperedge HE-8 (induced by source paper "latent topic conversational models"):
  with members:
    - "diversity promoting bayesian learning of latent variable models"
    - "latent intention dialogue models"
    - "referenceless quality estimation for natural language generation"
    - "generating high quality and informative conversation responses with sequence to sequence models"
    - "latent variable dialogue models and their diversity"
    - "steering output style and topic in neural response generation"
    - "discovering discrete latent topics with neural variational inference"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"

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)