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: "sub domain modelling for dialogue management with hierarchical reinforcement learning"
  Abstract: Human conversation is inherently complex, often spanning many different topics/domains. This makes policy learning for dialogue systems very challenging. Standard flat reinforcement learning methods do not provide an efficient framework for modelling such dialogues. In this paper, we focus on the under-explored problem of multi-domain dialogue management. First, we propose a new method for hierarchical reinforcement learning using the option framework. Next, we show that the proposed architecture learns faster and arrives at a better policy than the existing flat ones do. Moreover, we show how pretrained policies can be adapted to more complex systems with an additional set of new actions. In doing that, we show that our approach has the potential to facilitate policy optimisation for more sophisticated multi-domain dialogue systems.

Connected through hyperedge HE-1 (induced by source paper "a multimodal dialogue system for conversational image editing"):
  with members:
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "frames a corpus for adding memory to goal oriented dialogue systems"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "vision and language navigation interpreting visually grounded navigation instructions in real environments"
    - "a network based end to end trainable task oriented dialogue system"
    - "towards end to end reinforcement learning of dialogue agents for information access"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "hierarchical reinforcement learning for open domain dialog"):
  with members:
    - "do neural dialog systems use the conversation history effectively an empirical study"
    - "approximating interactive human evaluation with self play for open domain dialog systems"
    - "happybot generating empathetic dialogue responses by improving user experience look ahead"
    - "evaluating coherence in dialogue systems using entailment"
    - "universal adversarial triggers for attacking and analyzing nlp"
    - "what makes a good conversation how controllable attributes affect human judgments"
    - "deep reinforcement learning for modeling chit chat dialog with discrete attributes"
    - "way off policy batch deep reinforcement learning of implicit human preferences in dialog"

Connected through hyperedge HE-3 (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-4 (induced by source paper "budgeted policy learning for task oriented dialogue systems"):
  with members:
    - "microsoft dialogue challenge building end to end task completion dialogue systems"
    - "dialogue learning with human teaching and feedback in end to end trainable task oriented dialogue systems"
    - "switch based active deep dyna q efficient adaptive planning for task completion dialogue policy learning"
    - "integrating planning for task completion dialogue policy learning"
    - "neural approaches to conversational ai"
    - "discriminative deep dyna q robust planning for dialogue policy learning"
    - "iterative policy learning in end to end trainable task oriented neural dialog models"
    - "a tutorial on thompson sampling"

Connected through hyperedge HE-5 (induced by source paper "learn what not to learn action elimination with deep reinforcement learning"):
  with members:
    - "using reinforcement learning to learn how to play text based games"
    - "textworld a learning environment for text based games"
    - "counting to explore and generalize in text based games"
    - "efficient exploration through bayesian deep q networks"
    - "shallow updates for deep reinforcement learning"
    - "end to end task completion neural dialogue systems"
    - "a deep reinforcement learning chatbot"
    - "combating deep reinforcement learning s sisyphean curse with intrinsic fear"

Connected through hyperedge HE-6 (induced by source paper "integrating planning for task completion dialogue policy learning"):
  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"
    - "adversarial advantage actor critic model for task completion dialogue policy learning"
    - "end to end optimization of task oriented dialogue model with deep reinforcement learning"
    - "deep reinforcement learning for multi domain dialogue systems"
    - "towards end to end learning for dialog state tracking and management using deep reinforcement learning"
    - "the predictron end to end learning and planning"

Connected through hyperedge HE-7 (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"
    - "learning conversational systems that interleave task and non task content"
    - "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"
    - "towards end to end learning for dialog state tracking and management using deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "subgoal discovery for hierarchical dialogue policy learning"):
  with members:
    - "integrating planning for task completion dialogue policy learning"
    - "iterative policy learning in end to end trainable task oriented neural dialog models"
    - "feudal networks for hierarchical reinforcement learning"
    - "frames a corpus for adding memory to goal oriented dialogue systems"
    - "end to end task completion neural dialogue systems"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "adversarial advantage actor critic model for task completion dialogue policy learning"
    - "end to end optimization of task oriented dialogue model with deep reinforcement 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)