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: "reward shaping with recurrent neural networks for speeding up on line policy learning in spoken dialogue systems"
  Abstract: Statistical spoken dialogue systems have the attractive property of being able to be optimised from data via interactions with real users. However in the reinforcement learning paradigm the dialogue manager (agent) often requires significant time to explore the state-action space to learn to behave in a desirable manner. This is a critical issue when the system is trained on-line with real users where learning costs are expensive. Reward shaping is one promising technique for addressing these concerns. Here we examine three recurrent neural network (RNN) approaches for providing reward shaping information in addition to the primary (task-orientated) environmental feedback. These RNNs are trained on returns from dialogues generated by a simulated user and attempt to diffuse the overall evaluation of the dialogue back down to the turn level to guide the agent towards good behaviour faster. In both simulated and real user scenarios these RNNs are shown to increase policy learning speed. Importantly, they do not require prior knowledge of the user's goal.

Connected through hyperedge HE-1 (induced by source paper "air learning an ai research platform for algorithm hardware benchmarking of autonomous aerial robots"):
  with members:
    - "tencent ml images a large scale multi label image database for visual representation learning"
    - "mavbench micro aerial vehicle benchmarking"
    - "deep reinforcement learning for swarm systems"
    - "deep drone racing learning agile flight in dynamic environments"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "reinforcement learning for uav attitude control"
    - "learning navigation behaviors end to end with autorl"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-2 (induced by source paper "rudder return decomposition for delayed rewards"):
  with members:
    - "transfer in deep reinforcement learning using successor features and generalised policy improvement"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "optimizing agent behavior over long time scales by transporting value"
    - "meta gradient reinforcement learning"
    - "playing hard exploration games by watching youtube"
    - "forward backward reinforcement learning"
    - "world models"
    - "sparse attentive backtracking temporal creditassignment through reminding"

Connected through hyperedge HE-3 (induced by source paper "learning navigation behaviors end to end with autorl"):
  with members:
    - "barc backward reachability curriculum for robotic reinforcement learning"
    - "automated curriculum learning for neural networks"
    - "large scale evolution of image classifiers"
    - "reverse curriculum generation for reinforcement learning"
    - "intention net integrating planning and deep learning for goal directed autonomous navigation"
    - "from perception to decision a data driven approach to end to end motion planning for autonomous ground robots"
    - "prm rl long range robotic navigation tasks by combining reinforcement learning and sampling based planning"
    - "learning to reinforcement learn"

Connected through hyperedge HE-4 (induced by source paper "adversarial advantage actor critic model for task completion 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"
    - "towards end to end learning for dialog state tracking and management using deep reinforcement learning"
    - "generative adversarial imitation learning"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "towards end to end reinforcement learning of dialogue agents for information access"
    - "a user simulator for task completion dialogues"

Connected through hyperedge HE-5 (induced by source paper "learning end to end goal oriented dialog"):
  with members:
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "on the evaluation of dialogue systems with next utterance classification"
    - "end to end memory networks"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "evaluating prerequisite qualities for learning end to end dialog systems"
    - "a survey of available corpora for building data driven dialogue systems"
    - "the ubuntu dialogue corpus a large dataset for research in unstructured multi turn dialogue systems"
    - "towards ai complete question answering a set of prerequisite toy tasks"

Connected through hyperedge HE-6 (induced by source paper "dialog based language learning"):
  with members:
    - "end to end memory networks"
    - "a roadmap towards machine intelligence"
    - "evaluating prerequisite qualities for learning end to end dialog systems"
    - "show attend and tell neural image caption generation with visual attention"
    - "language understanding for text based games using deep reinforcement learning"
    - "action conditional video prediction using deep networks in atari games"
    - "towards ai complete question answering a set of prerequisite toy tasks"
    - "sequence level training with recurrent neural networks"

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.LG(Machine Learning)