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: "transfer in deep reinforcement learning using successor features and generalised policy improvement"
  Abstract: The ability to transfer skills across tasks has the potential to scale up reinforcement learning (RL) agents to environments currently out of reach. Recently, a framework based on two ideas, successor features (SFs) and generalised policy improvement (GPI), has been introduced as a principled way of transferring skills. In this paper we extend the SFs & GPI framework in two ways. One of the basic assumptions underlying the original formulation of SFs & GPI is that rewards for all tasks of interest can be computed as linear combinations of a fixed set of features. We relax this constraint and show that the theoretical guarantees supporting the framework can be extended to any set of tasks that only differ in the reward function. Our second contribution is to show that one can use the reward functions themselves as features for future tasks, without any loss of expressiveness, thus removing the need to specify a set of features beforehand. This makes it possible to combine SFs & GPI with deep learning in a more stable way. We empirically verify this claim on a complex 3D environment where observations are images from a first-person perspective. We show that the transfer promoted by SFs & GPI leads to very good policies on unseen tasks almost instantaneously. We also describe how to learn policies specialised to the new tasks in a way that allows them to be added to the agent's set of skills, and thus be reused in the future.

Connected through hyperedge HE-1 (induced by source paper "attentive multi task deep reinforcement learning"):
  with members:
    - "successor features support model based and model free reinforcement learning"
    - "reinforcement learning from imperfect demonstrations"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "playing hard exploration games by watching youtube"
    - "observe and look further achieving consistent performance on atari"
    - "kickstarting deep reinforcement learning"
    - "mix match agent curricula for reinforcement learning"
    - "progressive neural networks"

Connected through hyperedge HE-2 (induced by source paper "fast task inference with variational intrinsic successor features"):
  with members:
    - "model based reinforcement learning for atari"
    - "wasserstein dependency measure for representation learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "universal successor features approximators"
    - "large scale study of curiosity driven learning"
    - "diversity is all you need learning skills without a reward function"
    - "unsupervised control through non parametric discriminative rewards"
    - "rainbow combining improvements in deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "learning action transferable policy with action embedding"):
  with members:
    - "learning action representations for reinforcement learning"
    - "the natural language of actions"
    - "domain adaptation for reinforcement learning on the atari"
    - "combined reinforcement learning via abstract representations"
    - "learning latent dynamics for planning from pixels"
    - "deep contextualized word representations"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "universal successor representations for transfer reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "learning transferable domain priors for safe exploration in reinforcement learning"):
  with members:
    - "diverse exploration for fast and safe policy improvement"
    - "sample efficient reinforcement learning through transfer and architectural priors"
    - "learn what not to learn action elimination with deep reinforcement learning"
    - "investigating human priors for playing video games"
    - "kickstarting deep reinforcement learning"
    - "universal successor representations for transfer reinforcement learning"
    - "an optimal online method of selecting source policies for reinforcement learning"
    - "constrained policy optimization"

Connected through hyperedge HE-5 (induced by source paper "disentangled cumulants help successor representations transfer to new tasks"):
  with members:
    - "regularized hierarchical policies for compositional transfer in robotics"
    - "fast task inference with variational intrinsic successor features"
    - "visual reinforcement learning with imagined goals"
    - "diversity is all you need learning skills without a reward function"
    - "curiosity driven exploration of learned disentangled goal spaces"
    - "towards a definition of disentangled representations"
    - "eigenoption discovery through the deep successor representation"
    - "deep variational canonical correlation analysis"

Connected through hyperedge HE-6 (induced by source paper "transfer value iteration networks"):
  with members:
    - "gated path planning networks"
    - "extracting action sequences from texts based on deep reinforcement learning"
    - "value prediction network"
    - "cognitive mapping and planning for visual navigation"
    - "the predictron end to end learning and planning"
    - "value iteration networks"
    - "end to end goal driven web navigation"
    - "actor mimic deep multitask and transfer reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "better transfer learning with inferred successor maps"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "universal successor features approximators"
    - "experience replay for continual learning"
    - "universal successor representations for transfer reinforcement learning"
    - "selective experience replay for lifelong learning"
    - "advantages and limitations of using successor features for transfer in reinforcement learning"
    - "neural episodic control"
    - "distral robust multitask reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "rudder return decomposition for delayed rewards"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "optimizing agent behavior over long time scales by transporting value"
    - "meta gradient reinforcement learning"
    - "playing hard exploration games by watching youtube"
    - "forward backward reinforcement learning"
    - "world models"
    - "sparse attentive backtracking temporal creditassignment through reminding"
    - "methods for interpreting and understanding deep neural networks"

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)