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: "strategic dialogue management via deep reinforcement learning"
  Abstract: Artificially intelligent agents equipped with strategic skills that can negotiate during their interactions with other natural or artificial agents are still underdeveloped. This paper describes a successful application of Deep Reinforcement Learning (DRL) for training intelligent agents with strategic conversational skills, in a situated dialogue setting. Previous studies have modelled the behaviour of strategic agents using supervised learning and traditional reinforcement learning techniques, the latter using tabular representations or learning with linear function approximation. In this study, we apply DRL with a high-dimensional state space to the strategic board game of Settlers of Catan---where players can offer resources in exchange for others and they can also reply to offers made by other players. Our experimental results report that the DRL-based learnt policies significantly outperformed several baselines including random, rule-based, and supervised-based behaviours. The DRL-based policy has a 53% win rate versus 3 automated players (`bots'), whereas a supervised player trained on a dialogue corpus in this setting achieved only 27%, versus the same 3 bots. This result supports the claim that DRL is a promising framework for training dialogue systems, and strategic agents with negotiation abilities.

Connected through hyperedge HE-1 (induced by source paper "hierarchical context enhanced multi domain dialogue system for multi domain task completion"):
  with members:
    - "structured fusion networks for dialog"
    - "convlab multi domain end to end dialog system platform"
    - "onenet joint domain intent slot prediction for spoken language understanding"
    - "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"
    - "large scale multi domain belief tracking with knowledge sharing"
    - "scalable multi domain dialogue state tracking"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "multi issue bargaining with deep reinforcement learning"):
  with members:
    - "neural machine translation and sequence to sequence models a tutorial"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "continuous control with deep reinforcement learning"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "adversarial language games for advanced natural language intelligence"):
  with members:
    - "emergent tool use from multi agent autocurricula"
    - "better automatic evaluation of open domain dialogue systems with contextualized embeddings"
    - "on evaluation of adversarial perturbations for sequence to sequence models"
    - "universal adversarial triggers for attacking and analyzing nlp"
    - "egg a toolkit for research on emergence of language in games"
    - "robust neural machine translation with doubly adversarial inputs"
    - "adversarial attacks on deep learning models in natural language processing a survey"
    - "robust machine comprehension models via adversarial training"

Connected through hyperedge HE-4 (induced by source paper "agentgraph towards universal dialogue management with structured deep reinforcement learning"):
  with members:
    - "towards universal dialogue state tracking"
    - "sample efficient deep reinforcement learning for dialogue systems with large action spaces"
    - "feudal reinforcement learning for dialogue management in large domains"
    - "large scale multi domain belief tracking with knowledge sharing"
    - "rainbow combining improvements in deep reinforcement learning"
    - "non local neural networks"
    - "sample efficient actor critic reinforcement learning with supervised data for dialogue management"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "how to build user simulators to train rl based dialog systems"):
  with members:
    - "domain adaptive dialog generation via meta learning"
    - "unifying human and statistical evaluation for natural language generation"
    - "unsupervised dialog structure learning"
    - "convlab multi domain end to end dialog system platform"
    - "rethinking action spaces for reinforcement learning in end to end dialog agents with latent variable models"
    - "decoupling strategy and generation in negotiation dialogues"
    - "building a conversational agent overnight with dialogue self play"
    - "sentiment adaptive end to end dialog systems"

Connected through hyperedge HE-6 (induced by source paper "domain adaptive dialog generation via meta learning"):
  with members:
    - "few shot generalization across dialogue tasks"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "meta learning for low resource neural machine translation"
    - "sentiment adaptive end to end dialog systems"
    - "adversarial domain adaptation for variational neural language generation in dialogue systems"
    - "prototypical networks for few shot learning"
    - "a survey on dialogue systems recent advances and new frontiers"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-7 (induced by source paper "show price and negotiate a hierarchical attention recurrent visual negotiator"):
  with members:
    - "answerer in questioner s mind information theoretic approach to goal oriented visual dialog"
    - "talk the walk navigating new york city through grounded dialogue"
    - "decoupling strategy and generation in negotiation dialogues"
    - "coqa a conversational question answering challenge"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings"
    - "frames a corpus for adding memory to goal oriented dialogue systems"
    - "a survey on dialogue systems recent advances and new frontiers"

Connected through hyperedge HE-8 (induced by source paper "augmenting non collaborative dialog systems with explicit semantic and strategic dialog history"):
  with members:
    - "persuasion for good towards a personalized persuasive dialogue system for social good"
    - "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"

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)