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: "two geometric input transformation methods for fast online reinforcement learning with neural nets"
  Abstract: We apply neural nets with ReLU gates in online reinforcement learning. Our goal is to train these networks in an incremental manner, without the computationally expensive experience replay. By studying how individual neural nodes behave in online training, we recognize that the global nature of ReLU gates can cause undesirable learning interference in each node's learning behavior. We propose reducing such interferences with two efficient input transformation methods that are geometric in nature and match well the geometric property of ReLU gates. The first one is tile coding, a classic binary encoding scheme originally designed for local generalization based on the topological structure of the input space. The second one (EmECS) is a new method we introduce; it is based on geometric properties of convex sets and topological embedding of the input space into the boundary of a convex set. We discuss the behavior of the network when it operates on the transformed inputs. We also compare it experimentally with some neural nets that do not use the same input transformations, and with the classic algorithm of tile coding plus a linear function approximator, and on several online reinforcement learning tasks, we show that the neural net with tile coding or EmECS can achieve not only faster learning but also more accurate approximations. Our results strongly suggest that geometric input transformation of this type can be effective for interference reduction and takes us a step closer to fully incremental reinforcement learning with neural nets.

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"
    - "deep reinforcement learning and the deadly triad"
    - "the utility of sparse representations for control in reinforcement learning"
    - "learning sparse representations in reinforcement learning with sparse coding"

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"
    - "deep reinforcement learning and the deadly triad"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-3 (induced by source paper "deep reinforcement learning with decorrelation"):
  with members:
    - "quota the quantile option architecture for reinforcement learning"
    - "implicit quantile networks for distributional reinforcement learning"
    - "distributed distributional deterministic policy gradients"
    - "the utility of sparse representations for control in reinforcement learning"
    - "a distributional perspective on reinforcement learning"
    - "distributional reinforcement learning with quantile regression"
    - "revisiting the arcade learning environment evaluation protocols and open problems for general agents"
    - "reinforcement learning with unsupervised auxiliary tasks"

Connected through hyperedge HE-4 (induced by source paper "overcoming catastrophic interference in online reinforcement learning with dynamic self organizing maps"):
  with members:
    - "continual learning with self organizing maps"
    - "learning to learn without forgetting by maximizing transfer and minimizing interference"
    - "the utility of sparse representations for control in reinforcement learning"
    - "overcoming catastrophic forgetting in neural networks"
    - "unifying task specification in reinforcement learning"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-5 (induced by source paper "pseudo rehearsal achieving deep reinforcement learning without catastrophic forgetting"):
  with members:
    - "continual state representation learning for reinforcement learning using generative replay"
    - "progressive reinforcement learning with distillation for multi skilled motion control"
    - "experience replay for continual learning"
    - "learning to learn without forgetting by maximizing transfer and minimizing interference"
    - "deep generative dual memory network for continual learning"
    - "pseudo recursal solving the catastrophic forgetting problem in deep neural networks"
    - "selective experience replay for lifelong learning"
    - "continual reinforcement learning with complex synapses"

Connected through hyperedge HE-6 (induced by source paper "a multi objective deep reinforcement learning framework"):
  with members:
    - "online reinforcement learning in stochastic games"
    - "multi objective deep q learning with subsumption architecture"
    - "multi objective deep reinforcement learning"
    - "online reinforcement learning for real time exploration in continuous state and action markov decision processes"
    - "benchmarking deep reinforcement learning for continuous control"
    - "tensorflow a system for large scale machine learning"
    - "a survey of multi objective sequential decision making"

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)