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: "investigating generalisation in continuous deep reinforcement learning"
  Abstract: Deep Reinforcement Learning has shown great success in a variety of control tasks. However, it is unclear how close we are to the vision of putting Deep RL into practice to solve real world problems. In particular, common practice in the field is to train policies on largely deterministic simulators and to evaluate algorithms through training performance alone, without a train/test distinction to ensure models generalise and are not overfitted. Moreover, it is not standard practice to check for generalisation under domain shift, although robustness to such system change between training and testing would be necessary for real-world Deep RL control, for example, in robotics. In this paper we study these issues by first characterising the sources of uncertainty that provide generalisation challenges in Deep RL. We then provide a new benchmark and thorough empirical evaluation of generalisation challenges for state of the art Deep RL methods. In particular, we show that, if generalisation is the goal, then common practice of evaluating algorithms based on their training performance leads to the wrong conclusions about algorithm choice. Finally, we evaluate several techniques for improving generalisation and draw conclusions about the most robust techniques to date.

Connected through hyperedge HE-1 (induced by source paper "neuroevolution of self interpretable agents"):
  with members:
    - "rotation translation and cropping for zero shot generalization"
    - "skin lesion classification using cnns with patch based attention and diagnosis guided loss weighting"
    - "improving deep neuroevolution via deep innovation protection"
    - "leveraging procedural generation to benchmark reinforcement learning"
    - "emergent systematic generalization in a situated agent"
    - "continual learning with hypernetworks"
    - "learning to generalize from sparse and underspecified rewards"
    - "improving semantic segmentation of aerial images using patch based attention"

Connected through hyperedge HE-2 (induced by source paper "improving generalization of reinforcement learning with minimax distributional soft actor critic"):
  with members:
    - "addressing value estimation errors in reinforcement learning with a state action return distribution function"
    - "centralized conflict free cooperation for connected and automated vehicles at intersections by proximal policy optimization"
    - "risk averse robust adversarial reinforcement learning"
    - "a comparative analysis of expected and distributional reinforcement learning"
    - "composable deep reinforcement learning for robotic manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "risk sensitive reinforcement learning a constrained optimization viewpoint"
    - "assessing generalization in deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "analysing deep reinforcement learning agents trained with domain randomisation"):
  with members:
    - "emergent systematic generalization in a situated agent"
    - "learning dexterous in hand manipulation"
    - "an introduction to deep reinforcement learning"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "quantifying generalization in reinforcement learning"
    - "assessing generalization in deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "regularization matters in policy optimization"):
  with members:
    - "towards robust monocular depth estimation mixing datasets for zero shot cross dataset transfer"
    - "control regularization for reduced variance reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "generalization and regularization in dqn"
    - "meta reinforcement learning of structured exploration strategies"
    - "a study on overfitting in deep reinforcement learning"
    - "structured control nets for deep reinforcement learning"
    - "quantifying generalization in reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "generalization in transfer learning"):
  with members:
    - "a study on overfitting in deep reinforcement learning"
    - "structured control nets for deep reinforcement learning"
    - "quantifying generalization in reinforcement learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "benchmark environments for multitask learning in continuous domains"
    - "deep reinforcement learning that matters"
    - "progressive neural networks"

Connected through hyperedge HE-6 (induced by source paper "wasserstein robust reinforcement learning"):
  with members:
    - "compatible natural gradient policy search"
    - "non stationary markov decision processes a worst case approach using model based reinforcement learning"
    - "action robust reinforcement learning and applications in continuous control"
    - "deep lagrangian networks using physics as model prior for deep learning"
    - "beyond confidence regions tight bayesian ambiguity sets for robust mdps"
    - "deep lagrangian networks for end to end learning of energy based control for under actuated systems"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "lipschitz continuity in model based reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "efficient decorrelation of features using gramian in reinforcement learning"):
  with members:
    - "deep reinforcement learning with decorrelation"
    - "generalization and regularization in dqn"
    - "rainbow combining improvements in deep reinforcement learning"
    - "exploring generalization in deep learning"
    - "scalable and effective deep cca via soft decorrelation"
    - "a distributional perspective on reinforcement learning"
    - "opening the black box of deep neural networks via information"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-8 (induced by source paper "vrls a unified reinforcement learning scheduler for vehicle to vehicle communications"):
  with members:
    - "radio resource allocation for reliable out of coverage v2v communications"
    - "transfer learning for related reinforcement learning tasks via image to image translation"
    - "reinforcement learning scheduler for vehicle to vehicle communications outside coverage"
    - "asynchronous methods for deep reinforcement learning"
    - "playing atari with deep reinforcement 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)