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 "learning to guide guidance law based on deep meta learning and model predictive path integral control"):
  Abstract: In this paper, we present a novel guidance scheme based on model-based deep reinforcement learning (RL) technique. With model-based deep RL method, a deep neural network is trained as a predictive model of guidance dynamics which is incorporated into a model predictive path integral (MPPI) control framework. However, the traditional MPPI framework assumes the actual environment similar to the training dataset for the deep neural network which is impractical in practice with different maneuvering of the target, other perturbations, and actuator failures. To address this problem, our method utilizes meta-learning technique to make the deep neural dynamics model adapt to such changes online. With this approach, we can alleviate the performance deterioration of standard MPPI control caused by the difference between the actual environment and training data. Then, a novel guidance law for a varying velocity interceptor intercepting maneuvering target with desired terminal impact angle under actuator failure is constructed based on the aforementioned techniques. The simulation and experiment results under different cases show the effectiveness and robustness of the proposed guidance law in achieving successful interceptions of maneuvering target.

Includes members:
  Member "adaptive guidance with reinforcement meta learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reinforcement learning for angle only intercept guidance of maneuvering targets")
  Member "deep haptic model predictive control for robot assisted dressing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep imitation learning of sequential fabric smoothing from an algorithmic supervisor")
  Member "learning to adapt in dynamic real world environments through meta reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "revisiting meta learning as supervised learning")
  Member "learning to learn how to learn self adaptive visual navigation using meta learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "target driven visual navigation exploiting object relationships")
  Member "learning image conditioned dynamics models for control of under actuated legged millirobots":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep dynamics models for learning dexterous manipulation")
  Member "data efficient reinforcement learning with probabilistic model predictive control":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dynamics aware unsupervised discovery of skills")
  Member "neural network dynamics for model based deep reinforcement learning with model free fine tuning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement learning")
  Member "continuous adaptation via meta learning in nonstationary and competitive environments":
    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.RO(Robotics)