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 "vision based multi task manipulation for inexpensive robots using end to end learning from demonstration"):
  Abstract: We propose a technique for multi-task learning from demonstration that trains the controller of a low-cost robotic arm to accomplish several complex picking and placing tasks, as well as non-prehensile manipulation. The controller is a recurrent neural network using raw images as input and generating robot arm trajectories, with the parameters shared across the tasks. The controller also combines VAE-GAN-based reconstruction with autoregressive multimodal action prediction. Our results demonstrate that it is possible to learn complex manipulation tasks, such as picking up a towel, wiping an object, and depositing the towel to its previous position, entirely from raw images with direct behavior cloning. We show that weight sharing and reconstruction-based regularization substantially improve generalization and robustness, and training on multiple tasks simultaneously increases the success rate on all tasks.

Includes members:
  Member "from virtual demonstration to real world manipulation using lstm and mdn":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey of behavior learning applications in robotics state of the art and perspectives")
  Member "learning hand eye coordination for robotic grasping with deep learning and large scale data collection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scaling data driven robotics with reward sketching and batch reinforcement learning")
  Member "learning to push by grasping using multiple tasks for effective learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hierarchically decoupled imitation for morphological transfer")
  Member "learning to poke by poking experiential learning of intuitive physics":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning dynamic knowledge graphs to generalize on text based games")
  Member "learning modular neural network policies for multi task and multi robot transfer":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "citylearn diverse real world environments for sample efficient navigation policy learning")
  Member "pixel recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "robobarista object part based transfer of manipulation trajectories from crowd sourcing in 3d pointclouds":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sub goal trees a framework for goal based reinforcement learning")
  Member "continuous control with deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep reinforcement learning for autonomous driving a survey")

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)