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: "auto conditioned recurrent mixture density networks for complex trajectory generation"
  Abstract: Recent advancements in machine learning research have given rise to recurrent neural networks that are able to synthesize high-dimensional motion sequences over long time horizons. By leveraging these sequence learning techniques, we introduce a state transition model (STM) that is able to learn a variety of complex motion sequences in joint position space. Given few demonstrations from a motion planner, we show in real robot experiments that the learned STM can quickly generalize to unseen tasks. Our approach enables the robot to accomplish complex behaviors from high-level instructions that would require laborious hand-engineered sequencing of trajectories with traditional motion planners. #R##N#A video of our experiments is available at this https URL

Connected through hyperedge HE-1 (induced by source paper "sub goal trees a framework for goal based reinforcement learning"):
  with members:
    - "harnessing reinforcement learning for neural motion planning"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "motion planning networks"
    - "floyd warshall reinforcement learning learning from past experiences to reach new goals"
    - "hindsight experience replay"
    - "prediction and control with temporal segment models"
    - "proximal policy optimization algorithms"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"

Connected through hyperedge HE-2 (induced by source paper "harnessing reinforcement learning for neural motion planning"):
  with members:
    - "robot motion planning in learned latent spaces"
    - "universal planning networks"
    - "hierarchical reinforcement learning with hindsight"
    - "deep learning using rectified linear units relu"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "reinforced imitation sample efficient deep reinforcement learning for map less navigation by leveraging prior demonstrations"
    - "motion planning networks"
    - "deep sequential models for sampling based planning"

Connected through hyperedge HE-3 (induced by source paper "sub goal trees a framework for goal directed trajectory prediction and optimization"):
  with members:
    - "deep learning using rectified linear units relu"
    - "motion planning networks"
    - "floyd warshall reinforcement learning learning from past experiences to reach new goals"
    - "time agnostic prediction predicting predictable video frames"
    - "hindsight experience replay"
    - "prediction and control with temporal segment models"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"
    - "robobarista object part based transfer of manipulation trajectories from crowd sourcing in 3d pointclouds"

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)