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: "multi focus attention network for efficient deep reinforcement learning"
  Abstract: Deep reinforcement learning (DRL) has shown incredible performance in learning various tasks to the human level. However, unlike human perception, current DRL models connect the entire low-level sensory input to the state-action values rather than exploiting the relationship between and among entities that constitute the sensory input. Because of this difference, DRL needs vast amount of experience samples to learn. In this paper, we propose a Multi-focus Attention Network (MANet) which mimics human ability to spatially abstract the low-level sensory input into multiple entities and attend to them simultaneously. The proposed method first divides the low-level input into several segments which we refer to as partial states. After this segmentation, parallel attention layers attend to the partial states relevant to solving the task. Our model estimates state-action values using these attended partial states. In our experiments, MANet attains highest scores with significantly less experience samples. Additionally, the model shows higher performance compared to the Deep Q-network and the single attention model as benchmarks. Furthermore, we extend our model to attentive communication model for performing multi-agent cooperative tasks. In multi-agent cooperative task experiments, our model shows 20% faster learning than existing state-of-the-art model.

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"
    - "investigating generalisation in continuous deep reinforcement learning"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "self supervised discovering of causal features towards interpretable reinforcement learning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "explainable reinforcement learning through a causal lens"
    - "atari head atari human eye tracking and demonstration dataset"
    - "conservative q improvement reinforcement learning for an interpretable decision tree policy"
    - "free lunch saliency via attention in atari agents"
    - "reinforcement learning with attention that works a self supervised approach"
    - "towards interpretable reinforcement learning using attention augmented agents"
    - "agil learning attention from human for visuomotor tasks"

Connected through hyperedge HE-3 (induced by source paper "attention privileged reinforcement learning"):
  with members:
    - "solving rubik s cube with a robot hand"
    - "simultaneously learning vision and feature based control policies for real world ball in a cup"
    - "towards interpretable reinforcement learning using attention augmented agents"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "state representation learning for control an overview"
    - "shapestacks learning vision based physical intuition for generalised object stacking"
    - "on the continuity of rotation representations in neural networks"
    - "counterfactual multi agent policy gradients"

Connected through hyperedge HE-4 (induced by source paper "zero shot reinforcement learning with deep attention convolutional neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "reinforcement learning with attention that works a self supervised approach"
    - "learning 6 dof grasping and pick place using attention focus"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "sim to real learning agile locomotion for quadruped robots"
    - "vmav c a deep attention based reinforcement learning algorithm for model based control"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"

Connected through hyperedge HE-5 (induced by source paper "free lunch saliency via attention in atari agents"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "atari head atari human eye tracking and demonstration dataset"
    - "visual rationalizations in deep reinforcement learning for atari games"
    - "reinforcement learning with attention that works a self supervised approach"
    - "towards interpretable reinforcement learning using attention augmented agents"
    - "agil learning attention from human for visuomotor tasks"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "an introduction to deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "towards robust image classification using sequential attention models"):
  with members:
    - "natural adversarial examples"
    - "an alternative surrogate loss for pgd based adversarial testing"
    - "towards interpretable reinforcement learning using attention augmented agents"
    - "attention please adversarial defense via attention rectification and preservation"
    - "sequential attend infer repeat generative modelling of moving objects"
    - "feature denoising for improving adversarial robustness"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "adversarial logit pairing"

Connected through hyperedge HE-7 (induced by source paper "towards interpretable reinforcement learning using attention augmented agents"):
  with members:
    - "sequential attend infer repeat generative modelling of moving objects"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning visual question answering by bootstrapping hard attention"
    - "compositional attention networks for machine reasoning"
    - "cram clued recurrent attention model"
    - "a spatiotemporal model with visual attention for video classification"
    - "hierarchical attentive recurrent tracking"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "reinforcement learning with attention that works a self supervised approach"):
  with members:
    - "visual rationalizations in deep reinforcement learning for atari games"
    - "agil learning attention from human for visuomotor tasks"
    - "an initial attempt of combining visual selective attention with deep reinforcement learning"
    - "distributed prioritized experience replay"
    - "attention aware generative adversarial networks ata gans"
    - "explainable and explicit visual reasoning over scene graphs"
    - "non local neural networks"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"

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)