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: "learning sparse representations in reinforcement learning with sparse coding"
  Abstract: A variety of representation learning approaches have been investigated for reinforcement learning; much less attention, however, has been given to investigating the utility of sparse coding. Outside of reinforcement learning, sparse coding representations have been widely used, with non-convex objectives that result in discriminative representations. In this work, we develop a supervised sparse coding objective for policy evaluation. Despite the non-convexity of this objective, we prove that all local minima are global minima, making the approach amenable to simple optimization strategies. We empirically show that it is key to use a supervised objective, rather than the more straightforward unsupervised sparse coding approach. We compare the learned representations to a canonical fixed sparse representation, called tile-coding, demonstrating that the sparse coding representation outperforms a wide variety of tilecoding representations.

Connected through hyperedge HE-1 (induced by source paper "leaky tiling activations a simple approach to learning sparse representations online"):
  with members:
    - "meta learning representations for continual learning"
    - "learning sparse representations in reinforcement learning"
    - "learning action representations for reinforcement learning"
    - "representation learning on graphs a reinforcement learning application"
    - "continual state representation learning for reinforcement learning using generative replay"
    - "two geometric input transformation methods for fast online reinforcement learning with neural nets"
    - "deep reinforcement learning and the deadly triad"
    - "the utility of sparse representations for control in reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "deep tile coder an efficient sparse representation learning approach with applications in reinforcement learning"):
  with members:
    - "a theoretical analysis of deep q learning"
    - "learning sparse representations in reinforcement learning"
    - "learning action representations for reinforcement learning"
    - "representation learning on graphs a reinforcement learning application"
    - "continual state representation learning for reinforcement learning using generative replay"
    - "soft actor critic algorithms and applications"
    - "two geometric input transformation methods for fast online reinforcement learning with neural nets"
    - "deep reinforcement learning and the deadly triad"

Connected through hyperedge HE-3 (induced by source paper "learning sparse representations incrementally in deep reinforcement learning"):
  with members:
    - "a deeper look at experience replay"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "adam a method for stochastic optimization"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "the utility of sparse representations for control in reinforcement learning"):
  with members:
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "winner take all autoencoders"
    - "k sparse autoencoders"
    - "representation learning a review and new perspectives"

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.AI(Artificial Intelligence)