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: "persuasion for good towards a personalized persuasive dialogue system for social good"
  Abstract: Developing intelligent persuasive conversational agents to change people's opinions and actions for social good is the frontier in advancing the ethical development of automated dialogue systems. To do so, the first step is to understand the intricate organization of strategic disclosures and appeals employed in human persuasion conversations. We designed an online persuasion task where one participant was asked to persuade the other to donate to a specific charity. We collected a large dataset with 1,017 dialogues and annotated emerging persuasion strategies from a subset. Based on the annotation, we built a baseline classifier with context information and sentence-level features to predict the 10 persuasion strategies used in the corpus. Furthermore, to develop an understanding of personalized persuasion processes, we analyzed the relationships between individuals' demographic and psychological backgrounds including personality, morality, value systems, and their willingness for donation. Then, we analyzed which types of persuasion strategies led to a greater amount of donation depending on the individuals' personal backgrounds. This work lays the ground for developing a personalized persuasive dialogue system.

Connected through hyperedge HE-1 (induced by source paper "crwiz a framework for crowdsourcing real time wizard of oz dialogues"):
  with members:
    - "natural language interaction to facilitate mental models of remote robots"
    - "meetup a corpus of joint activity dialogues in a visual environment"
    - "end to end trainable non collaborative dialog system"
    - "the orca hub explainable offshore robotics through intelligent interfaces"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"
    - "learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings"
    - "frames a corpus for adding memory to goal oriented dialogue systems"
    - "edina building an open domain socialbot with self dialogues"

Connected through hyperedge HE-2 (induced by source paper "effects of persuasive dialogues testing bot identities and inquiry strategies"):
  with members:
    - "how to build user simulators to train rl based dialog systems"
    - "decoupling strategy and generation in negotiation dialogues"
    - "sentiment adaptive end to end dialog systems"
    - "learning to generate reviews and discovering sentiment"
    - "enriching word vectors with subword information"
    - "winning arguments interaction dynamics and persuasion strategies in good faith online discussions"

Connected through hyperedge HE-3 (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"
    - "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"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "alternating recurrent dialog model with large scale pre trained language models"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "the curious case of neural text degeneration"
    - "rethinking action spaces for reinforcement learning in end to end dialog agents with latent variable models"
    - "hello it s gpt 2 how can i help you towards the use of pretrained language models for task oriented dialogue systems"
    - "semantically conditioned dialog response generation via hierarchical disentangled self attention"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"

Connected through hyperedge HE-5 (induced by source paper "augmenting non collaborative dialog systems with explicit semantic and strategic dialog history"):
  with members:
    - "unsupervised dialog structure learning"
    - "a dynamic strategy coach for effective negotiation"
    - "decoupling strategy and generation in negotiation dialogues"
    - "tartan a retrieval based socialbot powered by a dynamic finite state machine architecture"
    - "sounding board a user centric and content driven social chatbot"
    - "learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings"
    - "combining search with structured data to create a more engaging user experience in open domain dialogue"
    - "a persona based neural conversation model"

Connected through hyperedge HE-6 (induced by source paper "moss end to end dialog system framework with modular supervision"):
  with members:
    - "dialogue learning with human teaching and feedback in end to end trainable task oriented dialogue systems"
    - "decoupling strategy and generation in negotiation dialogues"
    - "key value retrieval networks for task oriented dialogue"
    - "latent intention dialogue models"
    - "end to end task completion neural dialogue systems"
    - "deal or no deal end to end learning for negotiation dialogues"
    - "a network based end to end trainable task oriented dialogue system"
    - "incorporating copying mechanism in sequence to sequence learning"

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)