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: "from virtual demonstration to real world manipulation using lstm and mdn"
  Abstract: Robots assisting the disabled or elderly must perform complex manipulation tasks and must adapt to the home environment and preferences of their user. Learning from demonstration is a promising choice, that would allow the non-technical user to teach the robot different tasks. However, collecting demonstrations in the home environment of a disabled user is time consuming, disruptive to the comfort of the user, and presents safety challenges. It would be desirable to perform the demonstrations in a virtual environment. In this paper we describe a solution to the challenging problem of behavior transfer from virtual demonstration to a physical robot. The virtual demonstrations are used to train a deep neural network based controller, which is using a Long Short Term Memory (LSTM) recurrent neural network to generate trajectories. The training process uses a Mixture Density Network (MDN) to calculate an error signal suitable for the multimodal nature of demonstrations. The controller learned in the virtual environment is transferred to a physical robot (a Rethink Robotics Baxter). An off-the-shelf vision component is used to substitute for geometric knowledge available in the simulation and an inverse kinematics module is used to allow the Baxter to enact the trajectory. Our experimental studies validate the three contributions of the paper: (1) the controller learned from virtual demonstrations can be used to successfully perform the manipulation tasks on a physical robot, (2) the LSTM+MDN architectural choice outperforms other choices, such as the use of feedforward networks and mean-squared error based training signals and (3) allowing imperfect demonstrations in the training set also allows the controller to learn how to correct its manipulation mistakes.

Connected through hyperedge HE-1 (induced by source paper "a survey of behavior learning applications in robotics state of the art and perspectives"):
  with members:
    - "learning dexterous in hand manipulation"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "deep haptic model predictive control for robot assisted dressing"
    - "one shot visual imitation learning via meta learning"
    - "deep reinforcement learning a brief survey"
    - "learning complex dexterous manipulation with deep reinforcement learning and demonstrations"
    - "combining model based and model free updates for trajectory centric reinforcement learning"
    - "time contrastive networks self supervised learning from video"

Connected through hyperedge HE-2 (induced by source paper "accept synthetic objects as real end to end training of attentive deep visuomotor policies for manipulation in clutter"):
  with members:
    - "end to end robotic reinforcement learning without reward engineering"
    - "language as an abstraction for hierarchical deep reinforcement learning"
    - "pyrobot an open source robotics framework for research and benchmarking"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "craves controlling robotic arm with a vision based economic system"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "deep object centric representations for generalizable robot learning"
    - "learning complex dexterous manipulation with deep reinforcement learning and demonstrations"

Connected through hyperedge HE-3 (induced by source paper "contact skill imitation learning for robot independent assembly programming"):
  with members:
    - "learning robotic assembly from cad"
    - "deep reinforcement learning for high precision assembly tasks"
    - "end to end training of deep visuomotor policies"
    - "adam a method for stochastic optimization"
    - "show and tell a neural image caption generator"

Connected through hyperedge HE-4 (induced by source paper "auto conditioned recurrent mixture density networks for complex trajectory generation"):
  with members:
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "sim to real learning agile locomotion for quadruped robots"
    - "scaling simulation to real transfer by learning composable robot skills"
    - "multi modal imitation learning from unstructured demonstrations using generative adversarial nets"
    - "transferring end to end visuomotor control from simulation to real world for a multi stage task"
    - "3dof pedestrian trajectory prediction learned from long term autonomous mobile robot deployment data"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "auto conditioned recurrent networks for extended complex human motion synthesis"

Connected through hyperedge HE-5 (induced by source paper "avoidance of manual labeling in robotic autonomous navigation through multi sensory semi supervised learning"):
  with members:
    - "progressive neural networks"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "deep reinforcement learning an overview"
    - "from perception to decision a data driven approach to end to end motion planning for autonomous ground robots"
    - "virtual to real deep reinforcement learning continuous control of mobile robots for mapless navigation"
    - "cad2rl real single image flight without a single real image"
    - "end to end learning for self driving cars"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "vision based multi task manipulation for inexpensive robots using end to end learning from demonstration"):
  with members:
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "learning to push by grasping using multiple tasks for effective learning"
    - "learning to poke by poking experiential learning of intuitive physics"
    - "learning modular neural network policies for multi task and multi robot transfer"
    - "pixel recurrent neural networks"
    - "robobarista object part based transfer of manipulation trajectories from crowd sourcing in 3d pointclouds"
    - "continuous control with deep reinforcement learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)