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: "from eliza to xiaoice challenges and opportunities with social chatbots"
  Abstract: Conversational systems have come a long way after decades of research and development, from Eliza and Parry in the 60's and 70's, to task-completion systems as in the ATIS project, to intelligent personal assistants such as Siri, and to today's social chatbots like XiaoIce. Social chatbots' appeal lies in not only their ability to respond to users' diverse requests, but also in being able to establish an emotional connection with users. The latter is done by satisfying the users' essential needs for communication, affection, and social belonging. The design of social chatbots must focus on user engagement and take both intellectual quotient (IQ) and emotional quotient (EQ) into account. Users should want to engage with the social chatbot; as such, we define the success metric for social chatbots as conversation-turns per session (CPS). Using XiaoIce as an illustrative example, we discuss key technologies in building social chatbots from core chat to visual sense to skills. We also show how XiaoIce can dynamically recognize emotion and engage the user throughout long conversations with appropriate interpersonal responses. As we become the first generation of humans ever living with AI, social chatbots that are well-designed to be both useful and empathic will soon be ubiquitous.

Connected through hyperedge HE-1 (induced by source paper "low resource knowledge grounded dialogue generation"):
  with members:
    - "incremental transformer with deliberation decoder for document grounded conversations"
    - "mass masked sequence to sequence pre training for language generation"
    - "disentangling factors of variation using few labels"
    - "learning to select knowledge for response generation in dialog systems"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "deep contextualized word representations"
    - "isolating sources of disentanglement in variational autoencoders"

Connected through hyperedge HE-2 (induced by source paper "asking questions the human way scalable question answer generation from text corpus"):
  with members:
    - "addressing semantic drift in question generation for semi supervised question answering"
    - "generating question answer hierarchies"
    - "improving neural question generation using world knowledge"
    - "the curious case of neural text degeneration"
    - "learning to generate questions by learningwhat not to generate"
    - "question type driven question generation"
    - "multi task learning with language modeling for question generation"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-3 (induced by source paper "love is as complex as math metaphor generation system for social chatbot"):
  with members:
    - "touch your heart a tone aware chatbot for customer care on social media"
    - "deep reinforcement learning for dialogue generation"

Connected through hyperedge HE-4 (induced by source paper "low resource response generation with template prior"):
  with members:
    - "meta learning for low resource natural language generation in task oriented dialogue systems"
    - "text generation with exemplar based adaptive decoding"
    - "dual latent variable model for low resource natural language generation in dialogue systems"
    - "universal neural machine translation for extremely low resource languages"
    - "learning neural templates for text generation"
    - "conversational ai the science behind the alexa prize"
    - "generating sentences by editing prototypes"
    - "emotional chatting machine emotional conversation generation with internal and external memory"

Connected through hyperedge HE-5 (induced by source paper "neural response generation with meta words"):
  with members:
    - "what makes a good conversation how controllable attributes affect human judgments"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "a hierarchical latent structure for variational conversation modeling"
    - "content preserving text generation with attribute controls"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "toward controlled generation of text"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "hierarchical recurrent attention network for response generation"

Connected through hyperedge HE-6 (induced by source paper "personalized dialogue generation with diversified traits"):
  with members:
    - "training millions of personalized dialogue agents"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "addressee and response selection in multi party conversations with speaker interaction rnns"
    - "assigning personality identity to a chatting machine for coherent conversation generation"
    - "low rank rnn adaptation for context aware language modeling"
    - "personalization in goal oriented dialog"

Connected through hyperedge HE-7 (induced by source paper "read attend and comment a deep architecture for automatic news comment generation"):
  with members:
    - "challenges for toxic comment classification an in depth error analysis"
    - "know what you don t know unanswerable questions for squad"
    - "automatic article commenting the task and dataset"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "attention is all you need"
    - "get to the point summarization with pointer generator networks"
    - "s net from answer extraction to answer generation for machine reading comprehension"

Connected through hyperedge HE-8 (induced by source paper "learning a matching model with co teaching for multi turn response selection in retrieval based dialogue systems"):
  with members:
    - "learning matching models with weak supervision for response selection in retrieval based chatbots"
    - "co sampling training robust networks for extremely noisy supervision"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "alime assist an intelligent assistant for creating an innovative e commerce experience"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "a sequential matching framework for multi turn response selection in retrieval based chatbots"
    - "fidelity weighted learning"
    - "a persona based neural conversation model"

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.AI(Artificial Intelligence)