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: "continuous inverse optimal control with locally optimal examples"
  Abstract: Inverse optimal control, also known as inverse reinforcement learning, is the problem of recovering an unknown reward function in a Markov decision process from expert demonstrations of the optimal policy. We introduce a probabilistic inverse optimal control algorithm that scales gracefully with task dimensionality, and is suitable for large, continuous domains where even computing a full policy is impractical. By using a local approximation of the reward function, our method can also drop the assumption that the demonstrations are globally optimal, requiring only local optimality. This allows it to learn from examples that are unsuitable for prior methods.

Connected through hyperedge HE-1 (induced by source paper "generative adversarial imitation learning with neural networks global optimality and convergence rate"):
  with members:
    - "on computation and generalization of generative adversarial imitation learning"
    - "provably efficient reinforcement learning with linear function approximation"
    - "neural proximal trust region policy optimization attains globally optimal policy"
    - "an improved convergence analysis of stochastic variance reduced policy gradient"
    - "sample optimal parametric q learning using linearly additive features"
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "on the global convergence of imitation learning a case for linear quadratic regulator"
    - "policy optimization provably converges to nash equilibria in zero sum linear quadratic games"

Connected through hyperedge HE-2 (induced by source paper "quantifying hypothesis space misspecification in learning from human robot demonstrations and physical corrections"):
  with members:
    - "pragmatic pedagogic value alignment"
    - "autonomous interpretation of demonstrations for modification of dynamical movement primitives"
    - "risk aware active inverse reinforcement learning"
    - "an algorithmic perspective on imitation learning"
    - "variational inverse control with events a general framework for data driven reward definition"
    - "including uncertainty when learning from human corrections"
    - "learning under misspecified objective spaces"
    - "probabilistically safe robot planning with confidence based human predictions"

Connected through hyperedge HE-3 (induced by source paper "on computation and generalization of generative adversarial imitation learning"):
  with members:
    - "on the global convergence of imitation learning a case for linear quadratic regulator"
    - "learning temporal point processes via reinforcement learning"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "imitating driver behavior with generative adversarial networks"
    - "mmd gan towards deeper understanding of moment matching network"
    - "generalization and equilibrium in generative adversarial nets gans"
    - "proximal policy optimization algorithms"
    - "generative adversarial imitation learning"

Connected through hyperedge HE-4 (induced by source paper "less is more rethinking probabilistic models of human behavior"):
  with members:
    - "learning under misspecified objective spaces"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "maximum entropy deep inverse reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "exploiting variable impedance for energy efficient sequential movements"):
  with members:
    - "a hybrid dynamic regenerative damping scheme for energy regeneration in variable impedance actuators"
    - "energy regenerative damping in variable impedance actuators for long term robotic deployment"

Connected through hyperedge HE-6 (induced by source paper "a survey of deep learning applications to autonomous vehicle control"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "imminent collision mitigation with reinforcement learning and vision"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "causal confusion in imitation learning"
    - "once for all train one network and specialize it for efficient deployment on diverse hardware platforms"
    - "constrained deep neural network architecture search for iot devices accounting hardware calibration"
    - "a reinforcement learning based approach for automated lane change maneuvers"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"

Connected through hyperedge HE-7 (induced by source paper "energy based continuous inverse optimal control"):
  with members:
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "simulating emergent properties of human driving behavior using multi agent reward augmented imitation learning"
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "a survey of inverse reinforcement learning challenges methods and progress"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "simplifying reward design through divide and conquer"

Connected through hyperedge HE-8 (induced by source paper "learning reward functions by integrating human demonstrations and preferences"):
  with members:
    - "risk aware active inverse reinforcement learning"
    - "an efficient generalized bellman update for cooperative inverse reinforcement learning"
    - "simplifying reward design through divide and conquer"
    - "reward learning from human preferences and demonstrations in atari"
    - "batch active preference based learning of reward functions"
    - "inverse reward design"
    - "one shot imitation learning"
    - "proximal policy optimization algorithms"

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)