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 "model based action exploration"):
  Abstract: Deep reinforcement learning has great stride in solving challenging motion control tasks. #R##N#Recently there has been a significant amount of work on methods to exploit the data gathered during training, but less work is done on good methods for generating data to learn from. #R##N#For continuous actions domains, the typical method for generating exploratory actions is by sampling from a Gaussian distribution centred around the mean of a policy. #R##N#Although these methods can find an optimal policy, in practise, they do not scale well, and solving environments with many actions dimensions becomes impractical. #R##N#We consider learning a forward dynamics model to predict the result, ($s_{t+1}$), of taking a particular action, ($a$), given a specific observation of the state, ($s_{t}$). #R##N#With a model such as this we, can perform what comes more naturally to biological systems that have already collect experience, we perform internal predictions of outcomes and endeavour to try actions we believe have a reasonable chance of success. #R##N#This method greatly reduces the space of exploratory actions, increasing learning speed and enables higher quality solutions to difficult problems, such as robotic locomotion.

Includes members:
  Member "prediction and control with temporal segment models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sub goal trees a framework for goal based reinforcement learning")
  Member "curiosity driven exploration in deep reinforcement learning via bayesian neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scalable quantitative verification for deep neural networks")
  Member "learning and transfer of modulated locomotor controllers":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "following instructions by imagining and reaching visual goals")
  Member "the predictron end to end learning and planning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "policy aware model learning for policy gradient methods")
  Member "densely connected convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "lossless compression of deep neural networks")
  Member "asynchronous methods for deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "continuous deep q learning with model based acceleration":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "robust deep reinforcement learning against adversarial perturbations on observations")
  Member "value iteration networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "plannable approximations to mdp homomorphisms equivariance under actions")

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.AI(Artificial Intelligence)