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: "model free imitation learning with policy optimization"
  Abstract: In imitation learning, an agent learns how to behave in an environment with an unknown cost function by mimicking expert demonstrations. Existing imitation learning algorithms typically involve solving a sequence of planning or reinforcement learning problems. Such algorithms are therefore not directly applicable to large, high-dimensional environments, and their performance can significantly degrade if the planning problems are not solved to optimality. Under the apprenticeship learning formalism, we develop alternative model-free algorithms for finding a parameterized stochastic policy that performs at least as well as an expert policy on an unknown cost function, based on sample trajectories from the expert. Our approach, based on policy gradients, scales to large continuous environments with guaranteed convergence to local minima.

Connected through hyperedge HE-1 (induced by source paper "metric based imitation learning between two dissimilar anthropomorphic robotic arms"):
  with members:
    - "an algorithmic perspective on imitation learning"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "emergence of locomotion behaviours in rich environments"
    - "proximal policy optimization algorithms"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "tensorflow a system for large scale machine learning"
    - "learning monocular reactive uav control in cluttered natural environments"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

Connected through hyperedge HE-2 (induced by source paper "relational mimic for visual adversarial imitation learning"):
  with members:
    - "tactilegcn a graph convolutional network for predicting grasp stability with tactile sensors"
    - "stand alone self attention in vision models"
    - "the gan landscape losses architectures regularization and normalization"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning dexterous in hand manipulation"
    - "two stream adaptive graph convolutional networks for skeleton based action recognition"
    - "generative adversarial imitation from observation"
    - "spectral normalization for generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "hindsight trust region policy optimization"):
  with members:
    - "hierarchical reinforcement learning with hindsight"
    - "variance reduction for policy gradient with action dependent factorized baselines"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "visual reinforcement learning with imagined goals"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "hierarchical imitation and reinforcement learning"
    - "many goals reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "imitation learning as f divergence minimization"):
  with members:
    - "an algorithmic perspective on imitation learning"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "adversarial imitation via variational inverse reinforcement learning"
    - "sfv reinforcement learning of physical skills from videos"
    - "generative adversarial imitation from observation"
    - "sample efficient imitation learning via generative adversarial nets"
    - "variational discriminator bottleneck improving imitation learning inverse rl and gans by constraining information flow"
    - "fast policy learning through imitation and reinforcement"

Connected through hyperedge HE-5 (induced by source paper "model based behavioral cloning with future image similarity learning"):
  with members:
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "zero shot visual imitation"
    - "stochastic video generation with a learned prior"
    - "behavioral cloning from observation"
    - "imitation from observation learning to imitate behaviors from raw video via context translation"
    - "stochastic variational video prediction"
    - "dart noise injection for robust imitation learning"
    - "recurrent environment simulators"

Connected through hyperedge HE-6 (induced by source paper "bail best action imitation learning for batch deep reinforcement learning"):
  with members:
    - "stabilizing off policy q learning via bootstrapping error reduction"
    - "off policy evaluation via off policy classification"
    - "way off policy batch deep reinforcement learning of implicit human preferences in dialog"
    - "striving for simplicity in off policy deep reinforcement learning"
    - "reinforcement learning from imperfect demonstrations"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "off policy deep reinforcement learning without exploration"
    - "soft actor critic algorithms and applications"

Connected through hyperedge HE-7 (induced by source paper "multi agent adversarial inverse reinforcement learning"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "multi agent generative adversarial imitation learning"
    - "multi agent inverse reinforcement learning for general sum stochastic games"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "coordinated multi agent imitation learning"
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "infogail interpretable imitation learning from visual demonstrations"

Connected through hyperedge HE-8 (induced by source paper "deep local trajectory replanning and control for robot navigation"):
  with members:
    - "a deep learning based behavioral approach to indoor autonomous navigation"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "driving policy transfer via modularity and abstraction"
    - "mutan multimodal tucker fusion for visual question answering"
    - "simple online and realtime tracking with a deep association metric"
    - "social attention modeling attention in human crowds"
    - "from perception to decision a data driven approach to end to end motion planning for autonomous ground robots"
    - "socially aware motion planning 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)