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: "learning conversational systems that interleave task and non task content"
  Abstract: Task-oriented dialog systems have been applied in various tasks, such as automated personal assistants, customer service providers and tutors. These systems work well when users have clear and explicit intentions that are well-aligned to the systems' capabilities. However, they fail if users intentions are not explicit. To address this shortcoming, we propose a framework to interleave non-task content (i.e. everyday social conversation) into task conversations. When the task content fails, the system can still keep the user engaged with the non-task content. We trained a policy using reinforcement learning algorithms to promote long-turn conversation coherence and consistency, so that the system can have smooth transitions between task and non-task content. To test the effectiveness of the proposed framework, we developed a movie promotion dialog system. Experiments with human users indicate that a system that interleaves social and task content achieves a better task success rate and is also rated as more engaging compared to a pure task-oriented system.

Connected through hyperedge HE-1 (induced by source paper "if i hear you correctly building and evaluating interview chatbots with active listening skills"):
  with members:
    - "tell me about yourself using an ai powered chatbot to conduct conversational surveys"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "joint embedding of words and labels for text classification"
    - "touch your heart a tone aware chatbot for customer care on social media"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "learning to ask neural question generation for reading comprehension"
    - "axiomatic attribution for deep networks"

Connected through hyperedge HE-2 (induced by source paper "end to end trainable non collaborative dialog system"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "the curious case of neural text degeneration"
    - "moss end to end dialog system framework with modular supervision"
    - "persuasion for good towards a personalized persuasive dialogue system for social good"
    - "hello it s gpt 2 how can i help you towards the use of pretrained language models for task oriented dialogue systems"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "decoupling strategy and generation in negotiation dialogues"
    - "personalizing dialogue agents i have a dog do you have pets too"

Connected through hyperedge HE-3 (induced by source paper "generating more interesting responses in neural conversation models with distributional constraints"):
  with members:
    - "discourse aware neural rewards for coherent text generation"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "polite dialogue generation without parallel data"
    - "quac question answering in context"
    - "opennmt open source toolkit for neural machine translation"
    - "generating high quality and informative conversation responses with sequence to sequence models"
    - "towards an automatic turing test learning to evaluate dialogue responses"
    - "steering output style and topic in neural response generation"

Connected through hyperedge HE-4 (induced by source paper "multimodal hierarchical reinforcement learning policy for task oriented visual dialog"):
  with members:
    - "feudal reinforcement learning for dialogue management in large domains"
    - "evaluating visual conversational agents via cooperative human ai games"
    - "asking the difficult questions goal oriented visual question generation via intermediate rewards"
    - "image grounded conversations multimodal context for natural question and response generation"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "best of both worlds transferring knowledge from discriminative learning to a generative visual dialog model"
    - "sub domain modelling for dialogue management with hierarchical reinforcement learning"
    - "towards end to end learning for dialog state tracking and management using deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "sentiment adaptive end to end dialog systems"):
  with members:
    - "iterative policy learning in end to end trainable task oriented neural dialog models"
    - "end to end task completion neural dialogue systems"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "a copy augmented sequence to sequence architecture gives good performance on task oriented dialogue"
    - "an end to end trainable neural network model with belief tracking for task oriented dialog"
    - "a network based end to end trainable task oriented dialogue system"
    - "query regression networks for machine comprehension"
    - "towards end to end learning for dialog state tracking and management using deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "few shot generalization across dialogue tasks"):
  with members:
    - "can recurrent neural networks warp time"
    - "starspace embed all the things"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "rasa open source language understanding and dialogue management"
    - "a network based end to end trainable task oriented dialogue system"
    - "recurrent dropout without memory loss"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "learning end to end goal oriented dialog"

Connected through hyperedge HE-7 (induced by source paper "generative encoder decoder models for task oriented spoken dialog systems with chatting capability"):
  with members:
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "a copy augmented sequence to sequence architecture gives good performance on task oriented dialogue"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "deep reinforcement learning for dialogue generation"
    - "a network based end to end trainable task oriented dialogue system"
    - "towards end to end learning for dialog state tracking and management using deep reinforcement learning"
    - "end to end lstm based dialog control optimized with supervised and reinforcement learning"
    - "towards end to end reinforcement learning of dialogue agents for information access"

Connected through hyperedge HE-8 (induced by source paper "an ensemble model with ranking for social dialogue"):
  with members:
    - "towards an automatic turing test learning to evaluate dialogue responses"
    - "deep reinforcement learning for dialogue generation"
    - "a neural network approach to context sensitive generation of conversational responses"
    - "a neural conversational model"
    - "efficient estimation of word representations in vector space"
    - "chameleons in imagined conversations a new approach to understanding coordination of linguistic style in dialogs"

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)