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: "generalization and regularization in dqn"
  Abstract: Deep reinforcement learning (RL) algorithms have shown an impressive ability to learn complex control policies in high-dimensional environments. However, despite the ever-increasing performance on popular benchmarks such as the Arcade Learning Environment (ALE), policies learned by deep RL algorithms often struggle to generalize when evaluated in remarkably similar environments. In this paper, we assess the generalization capabilities of DQN, one of the most traditional deep RL algorithms in the field. We provide evidence suggesting that DQN overspecializes to the training environment. We comprehensively evaluate the impact of traditional regularization methods, $\ell_2$-regularization and dropout, and of reusing the learned representations to improve the generalization capabilities of DQN. We perform this study using different game modes of Atari 2600 games, a recently introduced modification for the ALE which supports slight variations of the Atari 2600 games traditionally used for benchmarking. Despite regularization being largely underutilized in deep RL, we show that it can, in fact, help DQN learn more general features. These features can then be reused and fine-tuned on similar tasks, considerably improving the sample efficiency of DQN.

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 "on catastrophic interference in atari 2600 games"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "continual unsupervised representation learning"
    - "continual lifelong learning with neural networks a review"
    - "ray interference a source of plateaus in deep reinforcement learning"
    - "benchmarking bonus based exploration methods on the arcade learning environment"
    - "dopamine a research framework for deep reinforcement learning"
    - "exploration by random network distillation"
    - "task free continual learning"

Connected through hyperedge HE-3 (induced by source paper "robust visual domain randomization for reinforcement learning"):
  with members:
    - "active domain randomization"
    - "generalization through simulation integrating simulated and real data into deep reinforcement learning for vision based autonomous flight"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "measuring and characterizing generalization in deep reinforcement learning"
    - "a study on overfitting in deep reinforcement learning"
    - "quantifying generalization in reinforcement learning"
    - "assessing generalization in deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "cyborg an autonomous cyber operations research gym"):
  with members:
    - "autonomous penetration testing using reinforcement learning"
    - "quantifying generalization in reinforcement learning"
    - "multi fidelity reinforcement learning with gaussian processes"
    - "virtual vs real trading off simulations and physical experiments in reinforcement learning with bayesian optimization"
    - "deep reinforcement learning with double q learning"
    - "the arcade learning environment an evaluation platform for general agents"
    - "simulating cyber attacks for fun and profit"

Connected through hyperedge HE-5 (induced by source paper "regularization matters in policy optimization"):
  with members:
    - "investigating generalisation in continuous deep reinforcement learning"
    - "towards robust monocular depth estimation mixing datasets for zero shot cross dataset transfer"
    - "control regularization for reduced variance reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "meta reinforcement learning of structured exploration strategies"
    - "a study on overfitting in deep reinforcement learning"
    - "structured control nets for deep reinforcement learning"
    - "quantifying generalization in reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "network randomization a simple technique for generalization in deep reinforcement learning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "environment probing interaction policies"
    - "domain randomization for active pose estimation"
    - "exploration by random network distillation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "playing hard exploration games by watching youtube"
    - "learning not to learn training deep neural networks with biased data"

Connected through hyperedge HE-7 (induced by source paper "on the generalization gap in reparameterizable reinforcement learning"):
  with members:
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "reinforcement learning with perturbed rewards"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "identifying generalization properties in neural networks"
    - "a study on overfitting in deep reinforcement learning"
    - "quantifying generalization in reinforcement learning"
    - "assessing generalization in deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "generalizing from a few environments in safety critical reinforcement learning"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning dexterous in hand manipulation"
    - "evaluating uncertainty quantification in end to end autonomous driving control"
    - "fingerprint policy optimisation for robust reinforcement learning"
    - "a study on overfitting in deep reinforcement learning"
    - "quantifying generalization in reinforcement learning"
    - "ai safety gridworlds"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"

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)