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: "apprenticeship learning using inverse reinforcement learning and gradient methods"
  Abstract: In this paper we propose a novel gradient algorithm to learn a policy from an expert's observed behavior assuming that the expert behaves optimally with respect to some unknown reward function of a Markovian Decision Problem. The algorithm's aim is to find a reward function such that the resulting optimal policy matches well the expert's observed behavior. The main difficulty is that the mapping from the parameters to policies is both nonsmooth and highly redundant. Resorting to subdifferentials solves the first difficulty, while the second one is over- come by computing natural gradients. We tested the proposed method in two artificial domains and found it to be more reliable and efficient than some previous methods.

Connected through hyperedge HE-1 (induced by source paper "learning navigation costs from demonstration in partially observable environments"):
  with members:
    - "differentiable algorithm networks for composable robot learning"
    - "path integral networks end to end differentiable optimal control"
    - "memory augmented control networks"
    - "qmdp net deep learning for planning under partial observability"
    - "reinforcement learning via recurrent convolutional neural networks"
    - "cognitive mapping and planning for visual navigation"
    - "watch this scalable cost function learning for path planning in urban environments"
    - "value iteration networks"

Connected through hyperedge HE-2 (induced by source paper "active task inference guided deep inverse reinforcement learning"):
  with members:
    - "taco learning task decomposition via temporal alignment for control"
    - "hirl hierarchical inverse reinforcement learning for long horizon tasks with delayed rewards"
    - "watch this scalable cost function learning for path planning in urban environments"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "maximum entropy deep inverse reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "interactive learning of environment dynamics for sequential tasks"):
  with members:
    - "inverse reinforcement learning with simultaneous estimation of rewards and dynamics"
    - "value iteration networks"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

Connected through hyperedge HE-4 (induced by source paper "an introduction to deep reinforcement learning"):
  with members:
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "playing hard exploration games by watching youtube"
    - "learning to play with intrinsically motivated self aware agents"
    - "horizon facebook s open source applied reinforcement learning platform"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "a study on overfitting in deep reinforcement learning"
    - "a dissection of overfitting and generalization in continuous reinforcement learning"
    - "rainbow combining improvements in deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "where do you think you re going inferring beliefs about dynamics from behavior"):
  with members:
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "learning models for shared control of human machine systems with unknown dynamics"
    - "shared autonomy via deep reinforcement learning"
    - "planning fast and slow a framework for adaptive real time safe trajectory planning"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "impossibility of deducing preferences and rationality from human policy"
    - "reinforcement learning with deep energy based policies"

Connected through hyperedge HE-6 (induced by source paper "machine teaching for inverse reinforcement learning algorithms and applications"):
  with members:
    - "risk aware active inverse reinforcement learning"
    - "a survey of inverse reinforcement learning challenges methods and progress"
    - "an overview of machine teaching"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "enabling robots to communicate their objectives"
    - "efficient probabilistic performance bounds for inverse reinforcement learning"
    - "preference based teaching"
    - "cooperative inverse reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "a survey of inverse reinforcement learning challenges methods and progress"):
  with members:
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "learning to cooperate via policy search"
    - "multi agent inverse reinforcement learning for zero sum games"
    - "computational rationalization the inverse equilibrium problem"
    - "continuous inverse optimal control with locally optimal examples"
    - "a framework for sequential planning in multi agent settings"
    - "the communicative multiagent team decision problem analyzing teamwork theories and models"
    - "reinforcement learning a survey"

Connected through hyperedge HE-8 (induced by source paper "inverse reinforcement learning via nonparametric spatio temporal subgoal modeling"):
  with members:
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "boltzmann exploration done right"
    - "hirl hierarchical inverse reinforcement learning for long horizon tasks with delayed rewards"
    - "bayesian reinforcement learning a survey"
    - "on the complexity of solving markov decision problems"

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)