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: "subgoal discovery for hierarchical dialogue policy learning"
  Abstract: Developing agents to engage in complex goal-oriented dialogues is challenging partly because the main learning signals are very sparse in long conversations. In this paper, we propose a divide-and-conquer approach that discovers and exploits the hidden structure of the task to enable efficient policy learning. First, given successful example dialogues, we propose the Subgoal Discovery Network (SDN) to divide a complex goal-oriented task into a set of simpler subgoals in an unsupervised fashion. We then use these subgoals to learn a multi-level policy by hierarchical reinforcement learning. We demonstrate our method by building a dialogue agent for the composite task of travel planning. Experiments with simulated and real users show that our approach performs competitively against a state-of-the-art method that requires human-defined subgoals. Moreover, we show that the learned subgoals are often human comprehensible.

Connected through hyperedge HE-1 (induced by source paper "target driven visual navigation exploiting object relationships"):
  with members:
    - "ok vqa a visual question answering benchmark requiring external knowledge"
    - "deduce diverse scene detection methods in unseen challenging environments"
    - "benchmarking classic and learned navigation in complex 3d environments"
    - "looking at the right stuff guided semantic gaze for autonomous driving"
    - "to learn or not to learn analyzing the role of learning for navigation in virtual environments"
    - "on evaluation of embodied navigation agents"
    - "learning to learn how to learn self adaptive visual navigation using meta learning"
    - "visual semantic navigation using scene priors"

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 "introducing mantis a novel multi domain information seeking dialogues dataset"):
  with members:
    - "simple applications of bert for ad hoc document retrieval"
    - "asking clarifying questions in open domain information seeking conversations"
    - "a hybrid retrieval generation neural conversation model"
    - "user intent prediction in information seeking conversations"
    - "interpretation of natural language rules in conversational machine reading"
    - "analyzing and characterizing user intent in information seeking conversations"
    - "integrating planning for task completion dialogue policy learning"
    - "learning to ask good questions ranking clarification questions using neural expected value of perfect information"

Connected through hyperedge HE-4 (induced by source paper "discriminative deep dyna q robust planning for 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"
    - "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"
    - "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"
    - "the predictron end to end learning and planning"
    - "towards end to end reinforcement learning of dialogue agents for information access"

Connected through hyperedge HE-5 (induced by source paper "multi level memory for task oriented dialogs"):
  with members:
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "relevance of unsupervised metrics in task oriented dialogue for evaluating natural language generation"
    - "key value retrieval networks for task oriented dialogue"
    - "a frame tracking model for memory enhanced dialogue systems"
    - "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"
    - "pointing the unknown words"

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)