human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "multi task maximum entropy inverse reinforcement learning"):
  Abstract: Multi-task Inverse Reinforcement Learning (IRL) is the problem of inferring multiple reward functions from expert demonstrations. Prior work, built on Bayesian IRL, is unable to scale to complex environments due to computational constraints. This paper contributes a formulation of multi-task IRL in the more computationally efficient Maximum Causal Entropy (MCE) IRL framework. Experiments show our approach can perform one-shot imitation learning in a gridworld environment that single-task IRL algorithms need hundreds of demonstrations to solve. We outline preliminary work using meta-learning to extend our method to the function approximator setting of modern MCE IRL algorithms. Evaluating on multi-task variants of common simulated robotics benchmarks, we discover serious limitations of these IRL algorithms, and conclude with suggestions for further work.

Includes members:
  Member "multi agent generative adversarial imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi agent interactions modeling with correlated policies")
  Member "multi modal imitation learning from unstructured demonstrations using generative adversarial nets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "machine learning approaches for motor learning a short review")
  Member "robust imitation of diverse behaviors":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "machine learning approaches for motor learning a short review")
  Member "repeated inverse reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "joint goal and strategy inference across heterogeneous demonstrators via reward network distillation")
  Member "infogail interpretable imitation learning from visual demonstrations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scaling data driven robotics with reward sketching and batch reinforcement learning")
  Member "one shot imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "revisiting meta learning as supervised learning")
  Member "generative adversarial imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "unrolled generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")

Question: Please tell me which class the source paper 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)