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: "a survey of deep network solutions for learning control in robotics from reinforcement to imitation"
  Abstract: Deep learning techniques have been widely applied, achieving state-of-the-art results in various fields of study. This survey focuses on deep learning solutions that target learning control policies for robotics applications. We carry out our discussions on the two main paradigms for learning control with deep networks: deep reinforcement learning and imitation learning. For deep reinforcement learning (DRL), we begin from traditional reinforcement learning algorithms, showing how they are extended to the deep context and effective mechanisms that could be added on top of the DRL algorithms. We then introduce representative works that utilize DRL to solve navigation and manipulation tasks in robotics. We continue our discussion on methods addressing the challenge of the reality gap for transferring DRL policies trained in simulation to real-world scenarios, and summarize robotics simulation platforms for conducting DRL research. For imitation leaning, we go through its three main categories, behavior cloning, inverse reinforcement learning and generative adversarial imitation learning, by introducing their formulations and their corresponding robotics applications. Finally, we discuss the open challenges and research frontiers.

Connected through hyperedge HE-1 (induced by source paper "map less navigation a single drl based controller for robots with varied dimensions"):
  with members:
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"
    - "soft actor critic algorithms and applications"
    - "learning with stochastic guidance for navigation"
    - "addressing function approximation error in actor critic methods"
    - "curiosity driven exploration for mapless navigation with deep reinforcement learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "learning with training wheels speeding up training with a simple controller for deep reinforcement learning"
    - "towards monocular vision based obstacle avoidance through deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "learning from interventions using hierarchical policies for safe learning"):
  with members:
    - "efficiently combining human demonstrations and interventions for safe training of autonomous systems in real time"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "cycle of learning for autonomous systems from human interaction"
    - "navigation by imitation in a pedestrian rich environment"
    - "hierarchical imitation and reinforcement learning"
    - "one shot hierarchical imitation learning of compound visuomotor tasks"
    - "hindsight experience replay"
    - "carla an open urban driving simulator"

Connected through hyperedge HE-3 (induced by source paper "learning to navigate in indoor environments from memorizing to reasoning"):
  with members:
    - "using rgb image as visual input for mapless robot navigation"
    - "composable deep reinforcement learning for robotic manipulation"
    - "semi parametric topological memory for navigation"
    - "on evaluation of embodied navigation agents"
    - "unifying map and landmark based representations for visual navigation"
    - "virtual to real deep reinforcement learning continuous control of mobile robots for mapless navigation"
    - "proximal policy optimization algorithms"
    - "reinforcement learning with unsupervised auxiliary tasks"

Connected through hyperedge HE-4 (induced by source paper "taming an autonomous surface vehicle for path following and collision avoidance using deep reinforcement learning"):
  with members:
    - "scipy 1 0 fundamental algorithms for scientific computing in python"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "scale sim systolic cnn accelerator simulator"):
  with members:
    - "euphrates algorithm soc co design for low power mobile continuous vision"
    - "attention is all you need"
    - "neural collaborative filtering"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "eie efficient inference engine on compressed deep neural network"
    - "designing energy efficient convolutional neural networks using energy aware pruning"
    - "you only look once unified real time object detection"
    - "deep speech 2 end to end speech recognition in english and mandarin"

Connected through hyperedge HE-6 (induced by source paper "coordinated heterogeneous distributed perception based on latent space representation"):
  with members:
    - "semi supervised learning with deep generative models"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "deep learning for iot big data and streaming analytics a survey"):
  with members:
    - "on accurate and reliable anomaly detection for gas turbine combustors a deep learning approach"
    - "enabling cognitive smart cities using big data and machine learning approaches and challenges"
    - "semisupervised deep reinforcement learning in support of iot and smart city services"
    - "in datacenter performance analysis of a tensor processing unit"
    - "an analysis of deep neural network models for practical applications"
    - "approxdbn approximate computing for discriminative deep belief networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "applying neural networks in optical communication systems possible pitfalls"

Connected through hyperedge HE-8 (induced by source paper "virtual to real deep reinforcement learning continuous control of mobile robots for mapless navigation"):
  with members:
    - "from perception to decision a data driven approach to end to end motion planning for autonomous ground robots"
    - "cad2rl real single image flight without a single real image"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"
    - "asynchronous methods for deep reinforcement learning"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"
    - "deep reinforcement learning with successor features for navigation across similar environments"
    - "towards cognitive exploration through deep reinforcement learning for mobile robots"
    - "learning to navigate in complex environments"

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.RO(Robotics)