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 "leaky tiling activations a simple approach to learning sparse representations online"):
  Abstract: Interference is a known problem when learning in online settings, such as continual learning or reinforcement learning. Interference occurs when updates, to improve performance for some inputs, degrades performance for others. Recent work has shown that sparse representations---where only a small percentage of units are active---can significantly reduce interference. Those works, however, relied on relatively complex regularization or meta-learning approaches, that have only been used offline in a pre-training phase. In our approach, we design an activation function that naturally produces sparse representations, and so is much more amenable to online training. The idea relies on the simple approach of binning, but overcomes the two key limitations of binning: zero gradients for the flat regions almost everywhere, and lost precision---reduced discrimination---due to coarse aggregation. We introduce a Leaky Tiling Activation (LTA) that provides non-negligible gradients and produces overlap between bins that improves discrimination. We empirically investigate both value-based and policy gradient reinforcement learning algorithms that use neural networks with LTAs, in classic discrete-action control environments and Mujoco continuous-action environments. We show that, with LTAs, learning is faster, with more stable policies, without needing target networks.

Includes members:
  Member "meta learning representations for continual learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual learning")
  Member "learning sparse representations in reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep tile coder an efficient sparse representation learning approach with applications in reinforcement learning")
  Member "learning action representations for reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "parameterizing branch and bound search trees to learn branching policies")
  Member "representation learning on graphs a reinforcement learning application":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "where are the keys learning object centric navigation policies on semantic maps with graph convolutional networks")
  Member "continual state representation learning for reinforcement learning using generative replay":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dynamics aware embeddings")
  Member "two geometric input transformation methods for fast online reinforcement learning with neural nets":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep tile coder an efficient sparse representation learning approach with applications in reinforcement learning")
  Member "deep reinforcement learning and the deadly triad":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a geometric perspective on optimal representations for reinforcement learning")
  Member "the utility of sparse representations for control in reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "interference and generalization in temporal difference learning")

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)