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: "feature control as intrinsic motivation for hierarchical reinforcement learning"
  Abstract: The problem of sparse rewards is one of the hardest challenges in contemporary reinforcement learning. Hierarchical reinforcement learning (HRL) tackles this problem by using a set of temporally-extended actions, or options, each of which has its own subgoal. These subgoals are normally handcrafted for specific tasks. Here, though, we introduce a generic class of subgoals with broad applicability in the visual domain. Underlying our approach (in common with work using "auxiliary tasks") is the hypothesis that the ability to control aspects of the environment is an inherently useful skill to have. We incorporate such subgoals in an end-to-end hierarchical reinforcement learning system and test two variants of our algorithm on a number of games from the Atari suite. We highlight the advantage of our approach in one of the hardest games -- Montezuma's revenge -- for which the ability to handle sparse rewards is key. Our agent learns several times faster than the current state-of-the-art HRL agent in this game, reaching a similar level of performance. UPDATE 22/11/17: We found that a standard A3C agent with a simple shaped reward, i.e. extrinsic reward + feature control intrinsic reward, has comparable performance to our agent in Montezuma Revenge. In light of the new experiments performed, the advantage of our HRL approach can be attributed more to its ability to learn useful features from intrinsic rewards rather than its ability to explore and reuse abstracted skills with hierarchical components. This has led us to a new conclusion about the result.

Connected through hyperedge HE-1 (induced by source paper "ride rewarding impact driven exploration for procedurally generated environments"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "torchbeast a pytorch platform for distributed rl"
    - "scheduled intrinsic drive a hierarchical take on intrinsically motivated exploration"
    - "craftassist instruction parsing semantic parsing for a minecraft assistant"
    - "unsupervised discovery of decision states for transfer in reinforcement learning"
    - "obstacle tower a generalization challenge in vision control and planning"
    - "count based exploration with the successor representation"
    - "exploration by random network distillation"

Connected through hyperedge HE-2 (induced by source paper "social navigation with human empowerment driven reinforcement learning"):
  with members:
    - "deep local trajectory replanning and control for robot navigation"
    - "a survey on intrinsic motivation in reinforcement learning"
    - "exploring applications of deep reinforcement learning for real world autonomous driving systems"
    - "combining optimal control and learning for visual navigation in novel environments"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "emi exploration with mutual information"
    - "intention net integrating planning and deep learning for goal directed autonomous navigation"

Connected through hyperedge HE-3 (induced by source paper "scheduled intrinsic drive a hierarchical take on intrinsically motivated exploration"):
  with members:
    - "count based exploration with the successor representation"
    - "exploration by random network distillation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "large scale study of curiosity driven learning"
    - "distributed prioritized experience replay"
    - "episodic curiosity through reachability"
    - "eigenoption discovery through the deep successor representation"
    - "feudal networks for hierarchical reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "autoqb automl for network quantization and binarization on mobile devices"):
  with members:
    - "amc automl for model compression and acceleration on mobile devices"
    - "alternating multi bit quantization for recurrent neural networks"
    - "discovering low precision networks close to full precision networks for efficient embedded inference"
    - "neural architecture search with bayesian optimisation and optimal transport"
    - "network sketching exploiting binary structure in deep cnns"
    - "learning transferable architectures for scalable image recognition"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "towards accurate binary convolutional neural network"

Connected through hyperedge HE-5 (induced by source paper "learning and exploiting multiple subgoals for fast exploration in hierarchical reinforcement learning"):
  with members:
    - "data efficient hierarchical reinforcement learning"
    - "observe and look further achieving consistent performance on atari"
    - "distributed prioritized experience replay"
    - "many goals reinforcement learning"
    - "feudal networks for hierarchical reinforcement learning"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "asynchronous methods for deep reinforcement learning"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"

Connected through hyperedge HE-6 (induced by source paper "contingency aware exploration in reinforcement learning"):
  with members:
    - "exploration by random network distillation"
    - "large scale study of curiosity driven learning"
    - "disentangling controllable and uncontrollable factors of variation by interacting with the world"
    - "on learning intrinsic rewards for policy gradient methods"
    - "diversity is all you need learning skills without a reward function"
    - "learning to navigate in cities without a map"
    - "parameter space noise for exploration"
    - "independently controllable features"

Connected through hyperedge HE-7 (induced by source paper "crawling in rogue s dungeons with partitioned a3c"):
  with members:
    - "generalization tower network a novel deep neural network architecture for multi task learning"
    - "feudal networks for hierarchical reinforcement learning"
    - "vizdoom a doom based ai research platform for visual reinforcement learning"
    - "sample efficient actor critic with experience replay"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "asynchronous methods for deep reinforcement learning"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "spatial transformer networks"

Connected through hyperedge HE-8 (induced by source paper "transferring end to end visuomotor control from simulation to real world for a multi stage task"):
  with members:
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "multi modal imitation learning from unstructured demonstrations using generative adversarial nets"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "third person imitation learning"
    - "learning invariant feature spaces to transfer skills with reinforcement learning"
    - "one shot imitation learning"
    - "deep visual foresight for planning robot motion"
    - "3d simulation for robot arm control with deep q learning"

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)