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: "towards characterizing divergence in deep q learning"
  Abstract: Deep Q-Learning (DQL), a family of temporal difference algorithms for control, employs three techniques collectively known as the `deadly triad' in reinforcement learning: bootstrapping, off-policy learning, and function approximation. Prior work has demonstrated that together these can lead to divergence in Q-learning algorithms, but the conditions under which divergence occurs are not well-understood. In this note, we give a simple analysis based on a linear approximation to the Q-value updates, which we believe provides insight into divergence under the deadly triad. The central point in our analysis is to consider when the leading order approximation to the deep-Q update is or is not a contraction in the sup norm. Based on this analysis, we develop an algorithm which permits stable deep Q-learning for continuous control without any of the tricks conventionally used (such as target networks, adaptive gradient optimizers, or using multiple Q functions). We demonstrate that our algorithm performs above or near state-of-the-art on standard MuJoCo benchmarks from the OpenAI Gym.

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"
    - "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"
    - "generalization and regularization in dqn"

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"
    - "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"
    - "is a good representation sufficient for sample efficient reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "improving performance in reinforcement learning by breaking generalization in neural networks"):
  with members:
    - "meta learning representations for continual learning"
    - "the utility of sparse representations for control in reinforcement 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-4 (induced by source paper "ac teach a bayesian actor critic method for policy learning with an ensemble of suboptimal teachers"):
  with members:
    - "crossnorm normalization for off policy td reinforcement learning"
    - "reinforcement learning from imperfect demonstrations"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "residual policy learning"
    - "learning dexterous in hand manipulation"
    - "off policy deep reinforcement learning without exploration"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "multi goal reinforcement learning challenging robotics environments and request for research"

Connected through hyperedge HE-5 (induced by source paper "a fine grained spectral perspective on neural networks"):
  with members:
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "frequency principle fourier analysis sheds light on deep neural networks"
    - "explicitizing an implicit bias of the frequency principle in two layer neural networks"
    - "a mean field theory of batch normalization"
    - "the convergence rate of neural networks for learned functions of different frequencies"
    - "on exact computation with an infinitely wide neural net"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "training behavior of deep neural network in frequency domain"

Connected through hyperedge HE-6 (induced by source paper "iris implicit reinforcement without interaction at scale for learning control from offline robot manipulation data"):
  with members:
    - "crossnorm normalization for off policy td reinforcement learning"
    - "off policy policy gradient with state distribution correction"
    - "stabilizing off policy q learning via bootstrapping error reduction"
    - "learning latent plans from play"
    - "way off policy batch deep reinforcement learning of implicit human preferences in dialog"
    - "goal conditioned imitation learning"
    - "striving for simplicity in off policy deep reinforcement learning"
    - "reinforcement learning from imperfect demonstrations"

Connected through hyperedge HE-7 (induced by source paper "temporal difference learning for nonlinear value function approximation in the lazy training regime"):
  with members:
    - "a theoretical analysis of deep q learning"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "neural temporal difference learning converges to global optima"
    - "on the expected dynamics of nonlinear td learning"
    - "geometric insights into the convergence of nonlinear td learning"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "neural tangent kernel convergence and generalization in neural networks"

Connected through hyperedge HE-8 (induced by source paper "the problem with ddpg understanding failures in deterministic environments with sparse rewards"):
  with members:
    - "q learning for continuous actions with cross entropy guided policies"
    - "exploiting the sign of the advantage function to learn deterministic policies in continuous domains"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "off policy deep reinforcement learning without exploration"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "soft actor critic algorithms and applications"
    - "addressing function approximation error in actor critic methods"
    - "deep reinforcement learning and the deadly triad"

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)