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 "deep tile coder an efficient sparse representation learning approach with applications in reinforcement learning"):
  Abstract: Representation learning is critical to the success of modern large-scale reinforcement learning systems. Previous works show that sparse representation can effectively reduce catastrophic interference and hence provide relatively stable and consistent boostrap targets when training reinforcement learning algorithms. Tile coding is a well-known sparse feature generation method in reinforcement learning. However, its application is largely restricted to small, low dimensional domains, as its computational and memory requirement grows exponentially as dimension increases. This paper proposes a simple and novel tile coding operation---deep tile coder, which adapts tile coding into deep learning setting, and can be easily scaled to high dimensional problems. The key distinction of our method with previous sparse representation learning method is that, we generate sparse feature by construction, while most previous works focus on designing regularization techniques. We are able to theoretically guarantee sparsity and importantly, our method ensures sparsity from the beginning of learning, without the need of tuning regularization weight. Furthermore, our approach maps from low dimension feature space to high dimension sparse feature space without introducing any additional training parameters. Our empirical demonstration covers classic discrete action control and Mujoco continuous robotics control problems. We show that reinforcement learning algorithms equipped with our deep tile coder achieves superior performance. To our best knowledge, our work is the first to demonstrate successful application of sparse representation learning method in online deep reinforcement learning algorithms for challenging tasks without using a target network.

Includes members:
  Member "a theoretical analysis of deep q learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning zero sum simultaneous move markov games using function approximation and correlated equilibrium")
  Member "learning sparse representations in reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "leaky tiling activations a simple approach to learning sparse representations online")
  Member "learning action representations for reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "leaky tiling activations a simple approach to learning sparse representations online")
  Member "representation learning on graphs a reinforcement learning application":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "leaky tiling activations a simple approach to learning sparse representations online")
  Member "continual state representation learning for reinforcement learning using generative replay":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "leaky tiling activations a simple approach to learning sparse representations online")
  Member "soft actor critic algorithms and applications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ready policy one world building through active learning")
  Member "two geometric input transformation methods for fast online reinforcement learning with neural nets":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "leaky tiling activations a simple approach to learning sparse representations online")
  Member "deep reinforcement learning and the deadly triad":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "leaky tiling activations a simple approach to learning sparse representations online")

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)