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: "how to discount deep reinforcement learning towards new dynamic strategies"
  Abstract: Using deep neural nets as function approximator for reinforcement learning tasks have recently been shown to be very powerful for solving problems approaching real-world complexity. Using these results as a benchmark, we discuss the role that the discount factor may play in the quality of the learning process of a deep Q-network (DQN). When the discount factor progressively increases up to its final value, we empirically show that it is possible to significantly reduce the number of learning steps. When used in conjunction with a varying learning rate, we empirically show that it outperforms original DQN on several experiments. We relate this phenomenon with the instabilities of neural networks when they are used in an approximate Dynamic Programming setting. We also describe the possibility to fall within a local optimum during the learning process, thus connecting our discussion with the exploration/exploitation dilemma.

Connected through hyperedge HE-1 (induced by source paper "automatic curriculum learning for deep rl a short survey"):
  with members:
    - "language as a cognitive tool to imagine goals in curiosity driven exploration"
    - "solving rubik s cube with a robot hand"
    - "active domain randomization"
    - "emergent tool use from multi agent autocurricula"
    - "distilling policy distillation"
    - "language grounding through social interactions and curiosity driven multi goal learning"
    - "automated curricula through setter solver interactions"
    - "continual learning for robotics"

Connected through hyperedge HE-2 (induced by source paper "fast efficient hyperparameter tuning for policy gradients"):
  with members:
    - "meta gradient reinforcement learning"
    - "bayesian optimization in alphago"
    - "deep variational reinforcement learning for pomdps"
    - "benchmarking reinforcement learning algorithms on real world robots"
    - "where did my optimum go an empirical analysis of gradient descent optimization in policy gradient methods"
    - "deep reinforcement learning that matters"
    - "treeqn and atreec differentiable tree structured models for deep reinforcement learning"
    - "hybrid reward architecture for reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "beyond exponentially discounted sum automatic learning of return function"):
  with members:
    - "separating value functions across time scales"
    - "rethinking the discount factor in reinforcement learning a decision theoretic approach"
    - "relational deep reinforcement learning"
    - "meta gradient reinforcement learning"
    - "on learning intrinsic rewards for policy gradient methods"
    - "rudder return decomposition for delayed rewards"
    - "attention is all you need"
    - "count based exploration in feature space for reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "metaoptimization on a distributed system for deep reinforcement learning"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "massively parallel hyperparameter tuning"
    - "accelerated methods for deep reinforcement learning"
    - "sample efficient reinforcement learning with stochastic ensemble value expansion"
    - "efficient hyperparameter optimization and infinitely many armed bandits"
    - "population based training of neural networks"
    - "asynchronous methods for deep reinforcement learning"
    - "reinforcement learning through asynchronous advantage actor critic on a gpu"

Connected through hyperedge HE-5 (induced by source paper "separating value functions across time scales"):
  with members:
    - "meta gradient reinforcement learning"
    - "how many random seeds statistical power analysis in deep reinforcement learning experiments"
    - "where did my optimum go an empirical analysis of gradient descent optimization in policy gradient methods"
    - "are deep policy gradient algorithms truly policy gradient algorithms"
    - "deep reinforcement learning that matters"
    - "hybrid reward architecture for reinforcement learning"
    - "optiongan learning joint reward policy options using generative adversarial inverse reinforcement learning"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-6 (induced by source paper "personalized heartsteps a reinforcement learning algorithm for optimizing physical activity"):
  with members:
    - "mitigating planner overfitting in model based reinforcement learning"
    - "a tutorial on thompson sampling"
    - "learning unknown markov decision processes a thompson sampling approach"
    - "encouraging physical activity in patients with diabetes intervention using a reinforcement learning system"
    - "misspecified linear bandits"
    - "data efficient off policy policy evaluation for reinforcement learning"
    - "doubly robust off policy value evaluation for reinforcement learning"
    - "learning to optimize via posterior sampling"

Connected through hyperedge HE-7 (induced by source paper "beyond the one step greedy approach in reinforcement learning"):
  with members:
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "asynchronous methods for deep reinforcement learning"
    - "lambda policy iteration a review and a new implementation"
    - "high dimensional continuous control using generalized advantage estimation"
    - "trust region policy optimization"
    - "performance bounds for lambda policy iteration and application to the game of tetris"

Connected through hyperedge HE-8 (induced by source paper "classification with costly features using deep reinforcement learning"):
  with members:
    - "rainbow combining improvements in deep reinforcement learning"
    - "asynchronous methods for deep reinforcement learning"
    - "continuous control with deep reinforcement learning"
    - "dueling network architectures for deep reinforcement learning"
    - "deep reinforcement learning with double q learning"
    - "adam a method for stochastic optimization"
    - "the greedy miser learning under test time budgets"
    - "datum wise classification a sequential approach to sparsity"

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)