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: "learning goal oriented visual dialog via tempered policy gradient"
  Abstract: Learning goal-oriented dialogues by means of deep reinforcement learning has recently become a popular research topic. However, commonly used policy-based dialogue agents often end up focusing on simple utterances and suboptimal policies. To mitigate this problem, we propose a class of novel temperature-based extensions for policy gradient methods, which are referred to as Tempered Policy Gradients (TPGs). On a recent AI-testbed, i.e., the GuessWhat?! game, we achieve significant improvements with two innovations. The first one is an extension of the state-of-the-art solutions with Seq2Seq and Memory Network structures that leads to an improvement of 7%. The second one is the application of our newly developed TPG methods, which improves the performance additionally by around 5% and, even more importantly, helps produce more convincing utterances.

Connected through hyperedge HE-1 (induced by source paper "mutual information based state control for intrinsically motivated reinforcement learning"):
  with members:
    - "skew fit state covering self supervised reinforcement learning"
    - "dynamical distance learning for unsupervised and semi supervised skill discovery"
    - "dynamical distance learning for semi supervised and unsupervised skill discovery"
    - "efficient dialog policy learning via positive memory retention"
    - "large scale study of curiosity driven learning"
    - "curiosity driven experience prioritization via density estimation"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "energy based hindsight experience prioritization"

Connected through hyperedge HE-2 (induced by source paper "what should i ask using conversationally informative rewards for goal oriented visual dialog"):
  with members:
    - "an active information seeking model for goal oriented vision and language tasks"
    - "learning end to end goal oriented dialog with multiple answers"
    - "jointly learning to see ask and guesswhat"
    - "question asking as program generation"
    - "best of both worlds transferring knowledge from discriminative learning to a generative visual dialog model"
    - "end to end optimization of goal driven and visually grounded dialogue systems"
    - "guesswhat visual object discovery through multi modal dialogue"
    - "bbq networks efficient exploration in deep reinforcement learning for task oriented dialogue systems"

Connected through hyperedge HE-3 (induced by source paper "efficient dialog policy learning via positive memory retention"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "sample efficient deep reinforcement learning for dialogue systems with large action spaces"
    - "evaluating visual conversational agents via cooperative human ai games"
    - "sample efficient actor critic reinforcement learning with supervised data for dialogue management"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "end to end task completion neural dialogue systems"
    - "visual reference resolution using attention memory for visual dialog"
    - "deal or no deal end to end learning for negotiation dialogues"

Connected through hyperedge HE-4 (induced by source paper "curiosity driven experience prioritization via density estimation"):
  with members:
    - "efficient dialog policy learning via positive memory retention"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "energy based hindsight experience prioritization"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "two stream rnn cnn for action recognition in 3d videos"
    - "curiosity driven exploration by self supervised prediction"
    - "learning to push by grasping using multiple tasks for effective learning"
    - "vime variational information maximizing exploration"

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)