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: "efficiently combining human demonstrations and interventions for safe training of autonomous systems in real time"
  Abstract: This paper investigates how to utilize different forms of human interaction to safely train autonomous systems in real-time by learning from both human demonstrations and interventions. We implement two components of the Cycle-of-Learning for Autonomous Systems, which is our framework for combining multiple modalities of human interaction. The current effort employs human demonstrations to teach a desired behavior via imitation learning, then leverages intervention data to correct for undesired behaviors produced by the imitation learner to teach novel tasks to an autonomous agent safely, after only minutes of training. We demonstrate this method in an autonomous perching task using a quadrotor with continuous roll, pitch, yaw, and throttle commands and imagery captured from a downward-facing camera in a high-fidelity simulated environment. Our method improves task completion performance for the same amount of human interaction when compared to learning from demonstrations alone, while also requiring on average 32% less data to achieve that performance. This provides evidence that combining multiple modes of human interaction can increase both the training speed and overall performance of policies for autonomous systems.

Connected through hyperedge HE-1 (induced by source paper "learning from interventions using hierarchical policies for safe learning"):
  with members:
    - "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"
    - "curiosity driven exploration by self supervised prediction"

Connected through hyperedge HE-2 (induced by source paper "grounding natural language commands to starcraft ii game states for narration guided reinforcement learning"):
  with members:
    - "on reinforcement learning for full length game of starcraft"
    - "follownet robot navigation by following natural language directions with deep reinforcement learning"
    - "following high level navigation instructions on a simulated quadcopter with imitation learning"
    - "reward learning from narrated demonstrations"
    - "accurately and efficiently interpreting human robot instructions of varying granularities"
    - "starcraft ii a new challenge for reinforcement learning"
    - "deep tamer interactive agent shaping in high dimensional state spaces"
    - "listen attend and walk neural mapping of navigational instructions to action sequences"

Connected through hyperedge HE-3 (induced by source paper "podnet a neural network for discovery of plannable options"):
  with members:
    - "inforl interpretable reinforcement learning using information maximization"
    - "diversity is all you need learning skills without a reward function"
    - "directed info gail learning hierarchical policies from unsegmented demonstrations using directed information"
    - "variational option discovery algorithms"
    - "robust imitation of diverse behaviors"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "end to end learning for self driving cars"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"

Connected through hyperedge HE-4 (induced by source paper "on the use of deep autoencoders for efficient embedded reinforcement learning"):
  with members:
    - "cycle of learning for autonomous systems from human interaction"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "deep tamer interactive agent shaping in high dimensional state spaces"
    - "proximal policy optimization algorithms"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "improving safety in reinforcement learning using model based architectures and human intervention"):
  with members:
    - "ai safety gridworlds"
    - "deep tamer interactive agent shaping in high dimensional state spaces"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "trust region policy optimization"

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.AI(Artificial Intelligence)