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 "directed info gail learning hierarchical policies from unsegmented demonstrations using directed information"):
  Abstract: The use of imitation learning to learn a single policy for a complex task that has multiple modes or hierarchical structure can be challenging. In fact, previous work has shown that when the modes are known, learning separate policies for each mode or sub-task can greatly improve the performance of imitation learning. In this work, we discover the interaction between sub-tasks from their resulting state-action trajectory sequences using a directed graphical model. We propose a new algorithm based on the generative adversarial imitation learning framework which automatically learns sub-task policies from unsegmented demonstrations. Our approach maximizes the directed information flow in the graphical model between sub-task latent variables and their generated trajectories. We also show how our approach connects with the existing Options framework, which is commonly used to learn hierarchical policies.

Includes members:
  Member "taco learning task decomposition via temporal alignment for control":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "active task inference guided deep inverse reinforcement learning")
  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 "ddco discovery of deep continuous options for robot learning from demonstrations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "applying depth sensing to automated surgical manipulation with a da vinci robot")
  Member "infogail interpretable imitation learning from visual demonstrations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "scaling data driven robotics with reward sketching and batch reinforcement learning")
  Member "multi level discovery of deep options":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "explore discover and learn unsupervised discovery of state covering skills")
  Member "proximal policy optimization algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  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 "modular multitask reinforcement learning with policy sketches":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "hierarchically decoupled imitation for morphological transfer")

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)