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: "hirl hierarchical inverse reinforcement learning for long horizon tasks with delayed rewards"
  Abstract: Reinforcement Learning (RL) struggles in problems with delayed rewards, and one approach is to segment the task into sub-tasks with incremental rewards. We propose a framework called Hierarchical Inverse Reinforcement Learning (HIRL), which is a model for learning sub-task structure from demonstrations. HIRL decomposes the task into sub-tasks based on transitions that are consistent across demonstrations. These transitions are defined as changes in local linearity w.r.t to a kernel function. Then, HIRL uses the inferred structure to learn reward functions local to the sub-tasks but also handle any global dependencies such as sequentiality. #R##N#We have evaluated HIRL on several standard RL benchmarks: Parallel Parking with noisy dynamics, Two-Link Pendulum, 2D Noisy Motion Planning, and a Pinball environment. In the parallel parking task, we find that rewards constructed with HIRL converge to a policy with an 80% success rate in 32% fewer time-steps than those constructed with Maximum Entropy Inverse RL (MaxEnt IRL), and with partial state observation, the policies learned with IRL fail to achieve this accuracy while HIRL still converges. We further find that that the rewards learned with HIRL are robust to environment noise where they can tolerate 1 stdev. of random perturbation in the poses in the environment obstacles while maintaining roughly the same convergence rate. We find that HIRL rewards can converge up-to 6x faster than rewards constructed with IRL.

Connected through hyperedge HE-1 (induced by source paper "oirl robust adversarial inverse reinforcement learning with temporally extended actions"):
  with members:
    - "directed info gail learning hierarchical policies from unsegmented demonstrations using directed information"
    - "multi modal imitation learning from unstructured demonstrations using generative adversarial nets"
    - "robust imitation of diverse behaviors"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "learnings options end to end for continuous action tasks"
    - "optiongan learning joint reward policy options using generative adversarial inverse reinforcement learning"
    - "proximal policy optimization algorithms"
    - "generative adversarial imitation learning"

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"
    - "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"
    - "apprenticeship learning using inverse reinforcement learning and gradient methods"

Connected through hyperedge HE-3 (induced by source paper "option compatible reward inverse reinforcement learning"):
  with members:
    - "composing meta policies for autonomous driving using hierarchical deep reinforcement learning"
    - "multi level discovery of deep options"
    - "optiongan learning joint reward policy options using generative adversarial inverse reinforcement learning"
    - "the option critic architecture"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "unsupervised perceptual rewards for imitation learning"
    - "hierarchical reinforcement learning with the maxq value function decomposition"

Connected through hyperedge HE-4 (induced by source paper "human interactive subgoal supervision for efficient inverse reinforcement learning"):
  with members:
    - "repeated inverse reinforcement learning"
    - "cooperative inverse reinforcement learning"
    - "incorporating human domain knowledge into large scale cost function learning"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

Connected through hyperedge HE-5 (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"
    - "bayesian reinforcement learning a survey"
    - "on the complexity of solving markov decision problems"
    - "apprenticeship learning using inverse reinforcement learning and gradient methods"

Connected through hyperedge HE-6 (induced by source paper "optiongan learning joint reward policy options using generative adversarial inverse reinforcement learning"):
  with members:
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "multi modal imitation learning from unstructured demonstrations using generative adversarial nets"
    - "hybrid reward architecture for reinforcement learning"
    - "robust imitation of diverse behaviors"
    - "learning human behaviors from motion capture by adversarial imitation"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "proximal policy optimization algorithms"
    - "generative adversarial imitation learning"

Connected through hyperedge HE-7 (induced by source paper "composing meta policies for autonomous driving using hierarchical deep reinforcement learning"):
  with members:
    - "benchmarking deep reinforcement learning for continuous control"
    - "trust region policy optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "playing atari with deep reinforcement learning"
    - "hierarchical reinforcement learning with the maxq value function decomposition"

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)