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: "multi task learning with deep model based reinforcement learning"
  Abstract: In recent years, model-free methods that use deep learning have achieved great success in many different reinforcement learning environments. Most successful approaches focus on solving a single task, while multi-task reinforcement learning remains an open problem. In this paper, we present a model based approach to deep reinforcement learning which we use to solve different tasks simultaneously. We show that our approach not only does not degrade but actually benefits from learning multiple tasks. For our model, we also present a new kind of recurrent neural network inspired by residual networks that decouples memory from computation allowing to model complex environments that do not require lots of memory. The code will be released before ICLR 2017.

Connected through hyperedge HE-1 (induced by source paper "efficient entropy for policy gradient with multidimensional action space"):
  with members:
    - "starcraft ii a new challenge for reinforcement learning"
    - "sobolev training for neural networks"
    - "learning multiagent communication with backpropagation"
    - "wavenet a generative model for raw audio"
    - "gradient estimation using stochastic computation graphs"
    - "adam a method for stochastic optimization"
    - "sequence to sequence learning with neural networks"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "self supervised deep reinforcement learning with generalized computation graphs for robot navigation"):
  with members:
    - "uncertainty aware reinforcement learning for collision avoidance"
    - "cad2rl real single image flight without a single real image"
    - "end to end learning for self driving cars"
    - "find your own way weakly supervised segmentation of path proposals for urban autonomy"
    - "on multiplicative integration with recurrent neural networks"
    - "action conditional video prediction using deep networks in atari games"
    - "deepdriving learning affordance for direct perception in autonomous driving"

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)