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 purposeful behaviour in the absence of rewards"
  Abstract: Artificial intelligence is commonly defined as the ability to achieve goals in the world. In the reinforcement learning framework, goals are encoded as reward functions that guide agent behaviour, and the sum of observed rewards provide a notion of progress. However, some domains have no such reward signal, or have a reward signal so sparse as to appear absent. Without reward feedback, agent behaviour is typically random, often dithering aimlessly and lacking intentionality. In this paper we present an algorithm capable of learning purposeful behaviour in the absence of rewards. The algorithm proceeds by constructing temporally extended actions (options), through the identification of purposes that are "just out of reach" of the agent's current behaviour. These purposes establish intrinsic goals for the agent to learn, ultimately resulting in a suite of behaviours that encourage the agent to visit different parts of the state space. Moreover, the approach is particularly suited for settings where rewards are very sparse, and such behaviours can help in the exploration of the environment until reward is observed.

Connected through hyperedge HE-1 (induced by source paper "clic curriculum learning and imitation for object control in non rewarding environments"):
  with members:
    - "learning actionable representations with goal conditioned policies"
    - "curriculum learning by transfer learning theory and experiments with deep networks"
    - "large scale study of curiosity driven learning"
    - "learning curriculum policies for reinforcement learning"
    - "diversity is all you need learning skills without a reward function"
    - "unsupervised control through non parametric discriminative rewards"
    - "many goals reinforcement learning"
    - "curious intrinsically motivated modular multi goal reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "discovering options for exploration by minimizing cover time"):
  with members:
    - "visual reinforcement learning with imagined goals"
    - "finding options that minimize planning time"
    - "diversity is all you need learning skills without a reward function"
    - "eigenoption discovery through the deep successor representation"
    - "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"
    - "the option critic architecture"

Connected through hyperedge HE-3 (induced by source paper "unsupervised control through non parametric discriminative rewards"):
  with members:
    - "universal planning networks"
    - "hierarchical reinforcement learning with hindsight"
    - "deepmind control suite"
    - "data efficient hierarchical reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "visual reinforcement learning with imagined goals"
    - "zero shot visual imitation"
    - "distributed prioritized experience replay"

Connected through hyperedge HE-4 (induced by source paper "learning representations in model free hierarchical reinforcement learning"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "feudal networks for hierarchical reinforcement learning"
    - "multi level discovery of deep options"
    - "a laplacian framework for option discovery in reinforcement learning"
    - "option discovery in hierarchical reinforcement learning using spatio temporal clustering"
    - "the option critic architecture"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "unifying count based exploration and intrinsic motivation"

Connected through hyperedge HE-5 (induced by source paper "learning to look around intelligently exploring unseen environments for unknown tasks"):
  with members:
    - "im2pano3d extrapolating 360 structure and semantics beyond the field of view"
    - "colorization as a proxy task for visual understanding"
    - "a dataset for developing and benchmarking active vision"
    - "hierarchical surface prediction for 3d object reconstruction"
    - "deep 360 pilot learning a deep agent for piloting through 360 deg sports video"
    - "look ahead before you leap end to end active recognition by forecasting the effect of motion"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "a laplacian framework for option discovery in reinforcement learning"):
  with members:
    - "option discovery in hierarchical reinforcement learning using spatio temporal clustering"
    - "the option critic architecture"
    - "strategic attentive writer for learning macro actions"
    - "control of memory active perception and action in minecraft"
    - "empowerment an introduction"
    - "the arcade learning environment an evaluation platform for general agents"
    - "active learning of inverse models with intrinsically motivated goal exploration in robots"
    - "hierarchical reinforcement learning with the maxq value function decomposition"

Connected through hyperedge HE-7 (induced by source paper "a framework for searching for general artificial intelligence"):
  with members:
    - "progressive neural networks"
    - "convolutional neural fabrics"
    - "online adaptation of deep architectures with reinforcement learning"
    - "feedforward initialization for fast inference of deep generative networks is biologically plausible"
    - "deep vs shallow networks an approximation theory perspective"
    - "programming with a differentiable forth interpreter"
    - "why artificial intelligence needs a task theory and what it might look like"
    - "adaptive learning with robust generalization guarantees"

Connected through hyperedge HE-8 (induced by source paper "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"):
  with members:
    - "deep exploration via bootstrapped dqn"
    - "understanding visual concepts with continuation learning"
    - "building machines that learn and think like people"
    - "attend infer repeat fast scene understanding with generative models"
    - "prioritized experience replay"
    - "deep convolutional inverse graphics network"
    - "language understanding for text based games using deep reinforcement learning"
    - "efficient inference in occlusion aware generative models of images"

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)