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: "the utility of sparse representations for control in reinforcement learning"
  Abstract: We investigate sparse representations for control in reinforcement learning. While these representations are widely used in computer vision, their prevalence in reinforcement learning is limited to sparse coding where extracting representations for new data can be computationally intensive. Here, we begin by demonstrating that learning a control policy incrementally with a representation from a standard neural network fails in classic control domains, whereas learning with a representation obtained from a neural network that has sparsity properties enforced is effective. We provide evidence that the reason for this is that the sparse representation provides locality, and so avoids catastrophic interference, and particularly keeps consistent, stable values for bootstrapping. We then discuss how to learn such sparse representations. We explore the idea of Distributional Regularizers, where the activation of hidden nodes is encouraged to match a particular distribution that results in sparse activation across time. We identify a simple but effective way to obtain sparse representations, not afforded by previously proposed strategies, making it more practical for further investigation into sparse representations for reinforcement learning.

Connected through hyperedge HE-1 (induced by source paper "interference and generalization in temporal difference learning"):
  with members:
    - "generalization guarantees for neural networks via harnessing the low rank structure of the jacobian"
    - "towards characterizing divergence in deep q learning"
    - "ray interference a source of plateaus in deep reinforcement learning"
    - "stiffness a new perspective on generalization in neural networks"
    - "pytorch an imperative style high performance deep learning library"
    - "striving for simplicity in off policy deep reinforcement learning"
    - "on first order meta learning algorithms"
    - "natural environment benchmarks for reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "discor corrective feedback in reinforcement learning via distribution correction"):
  with members:
    - "gradient surgery for multi task learning"
    - "diagnosing bottlenecks in deep q learning algorithms"
    - "towards characterizing divergence in deep q learning"
    - "behavior regularized offline reinforcement learning"
    - "meta world a benchmark and evaluation for multi task and meta reinforcement learning"
    - "stabilizing off policy q learning via bootstrapping error reduction"
    - "provably efficient q learning with function approximation via distribution shift error checking oracle"
    - "ray interference a source of plateaus in deep reinforcement learning"

Connected through hyperedge HE-3 (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"
    - "learning sparse representations in reinforcement learning with sparse coding"

Connected through hyperedge HE-4 (induced by source paper "improving performance in reinforcement learning by breaking generalization in neural networks"):
  with members:
    - "meta learning representations for continual learning"
    - "towards characterizing divergence in deep q learning"
    - "a deeper look at experience replay"
    - "neural map structured memory for deep reinforcement learning"
    - "towards generalization and simplicity in continuous control"
    - "overcoming catastrophic forgetting in neural networks"
    - "benchmarking deep reinforcement learning for continuous control"
    - "multi timescale nexting in a reinforcement learning robot"

Connected through hyperedge HE-5 (induced by source paper "meta learning representations for continual learning"):
  with members:
    - "gradient based sample selection for online continual learning"
    - "continual learning with tiny episodic memories"
    - "a meta transfer objective for learning to disentangle causal mechanisms"
    - "rotate your networks better weight consolidation and less catastrophic forgetting"
    - "efficient lifelong learning with a gem"
    - "meta learning update rules for unsupervised representation learning"
    - "learning to learn without forgetting by maximizing transfer and minimizing interference"
    - "deep online learning via meta learning continual adaptation for model based rl"

Connected through hyperedge HE-6 (induced by source paper "learning action transferable policy with action embedding"):
  with members:
    - "transfer in deep reinforcement learning using successor features and generalised policy improvement"
    - "learning action representations for reinforcement learning"
    - "the natural language of actions"
    - "domain adaptation for reinforcement learning on the atari"
    - "combined reinforcement learning via abstract representations"
    - "learning latent dynamics for planning from pixels"
    - "deep contextualized word representations"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-7 (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-8 (induced by source paper "diagnosing bottlenecks in deep q learning algorithms"):
  with members:
    - "provably efficient maximum entropy exploration"
    - "soft actor critic algorithms and applications"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "addressing function approximation error in actor critic methods"
    - "deep reinforcement learning and the deadly triad"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "policy optimization via importance sampling"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"

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.LG(Machine Learning)