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: "learnings options end to end for continuous action tasks"
  Abstract: We present new results on learning temporally extended actions for continuoustasks, using the options framework (Suttonet al.[1999b], Precup [2000]). In orderto achieve this goal we work with the option-critic architecture (Baconet al.[2017])using a deliberation cost and train it with proximal policy optimization (Schulmanet al.[2017]) instead of vanilla policy gradient. Results on Mujoco domains arepromising, but lead to interesting questions aboutwhena given option should beused, an issue directly connected to the use of initiation sets.

Connected through hyperedge HE-1 (induced by source paper "efficient deep reinforcement learning through policy transfer"):
  with members:
    - "the termination critic"
    - "deepmind control suite"
    - "kickstarting deep reinforcement learning"
    - "context aware policy reuse"
    - "multi modal imitation learning from unstructured demonstrations using generative adversarial nets"
    - "learning to compose skills"
    - "an optimal online method of selecting source policies for reinforcement learning"
    - "when waiting is not an option learning options with a deliberation cost"

Connected through hyperedge HE-2 (induced by source paper "oirl robust adversarial inverse reinforcement learning with temporally extended actions"):
  with members:
    - "directed info gail learning hierarchical policies from unsegmented demonstrations using directed information"
    - "multi modal imitation learning from unstructured demonstrations using generative adversarial nets"
    - "robust imitation of diverse behaviors"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "optiongan learning joint reward policy options using generative adversarial inverse reinforcement learning"
    - "proximal policy optimization algorithms"
    - "hirl hierarchical inverse reinforcement learning for long horizon tasks with delayed rewards"
    - "generative adversarial imitation learning"

Connected through hyperedge HE-3 (induced by source paper "options of interest temporal abstraction with interest functions"):
  with members:
    - "the termination critic"
    - "feudal networks for hierarchical reinforcement learning"
    - "when waiting is not an option learning options with a deliberation cost"
    - "a laplacian framework for option discovery in reinforcement learning"
    - "adaptive skills adaptive partitions asap"
    - "option discovery in hierarchical reinforcement learning using spatio temporal clustering"
    - "the option critic architecture"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"

Connected through hyperedge HE-4 (induced by source paper "learning to locomote with deep neural network and cpg based control in a soft snake robot"):
  with members:
    - "a validated physical model for real time simulation of soft robotic snakes"
    - "learning agile and dynamic motor skills for legged robots"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "proximal policy optimization algorithms"
    - "the option critic architecture"

Connected through hyperedge HE-5 (induced by source paper "composing diverse policies for temporally extended tasks"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "task agnostic dynamics priors for deep reinforcement learning"
    - "using causal analysis to learn specifications from task demonstrations"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "deepmdp learning continuous latent space models for representation learning"
    - "from explanation to synthesis compositional program induction for learning from demonstration"
    - "reinforcement learning on variable impedance controller for high precision robotic assembly"
    - "learning safe policies with expert guidance"

Connected through hyperedge HE-6 (induced by source paper "on the role of weight sharing during deep option learning"):
  with members:
    - "routing networks and the challenges of modular and compositional computation"
    - "reinforcement learning without backpropagation or a clock"
    - "learning abstract options"
    - "how many random seeds statistical power analysis in deep reinforcement learning experiments"
    - "learning to learn without forgetting by maximizing transfer and minimizing interference"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "progressive neural networks"
    - "routing networks adaptive selection of non linear functions for multi task learning"

Connected through hyperedge HE-7 (induced by source paper "dac the double actor critic architecture for learning options"):
  with members:
    - "generalized off policy actor critic"
    - "deepmind control suite"
    - "learning abstract options"
    - "learning latent dynamics for planning from pixels"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "ace an actor ensemble algorithm for continuous control with tree search"
    - "eigenoption discovery through the deep successor representation"
    - "feudal networks for hierarchical reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "dynoplan combining motion planning and deep neural network based controllers for safe hrl"):
  with members:
    - "risk aware active inverse reinforcement learning"
    - "learning safe policies with expert guidance"
    - "learning robotic assembly from cad"
    - "a survey of inverse reinforcement learning challenges methods and progress"
    - "learning with options that terminate off policy"
    - "when waiting is not an option learning options with a deliberation cost"

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)