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: "a greedy approach to adapting the trace parameter for temporal difference learning"
  Abstract: One of the main obstacles to broad application of reinforcement learning methods is the parameter sensitivity of our core learning algorithms. In many large-scale applications, online computation and function approximation represent key strategies in scaling up reinforcement learning algorithms. In this setting, we have effective and reasonably well understood algorithms for adapting the learning-rate parameter, online during learning. Such meta-learning approaches can improve robustness of learning and enable specialization to current task, improving learning speed. For temporal-difference learning algorithms which we study here, there is yet another parameter, $\lambda$, that similarly impacts learning speed and stability in practice. Unfortunately, unlike the learning-rate parameter, $\lambda$ parametrizes the objective function that temporal-difference methods optimize. Different choices of $\lambda$ produce different fixed-point solutions, and thus adapting $\lambda$ online and characterizing the optimization is substantially more complex than adapting the learning-rate parameter. There are no meta-learning method for $\lambda$ that can achieve (1) incremental updating, (2) compatibility with function approximation, and (3) maintain stability of learning under both on and off-policy sampling. In this paper we contribute a novel objective function for optimizing $\lambda$ as a function of state rather than time. We derive a new incremental, linear complexity $\lambda$-adaption algorithm that does not require offline batch updating or access to a model of the world, and present a suite of experiments illustrating the practicality of our new algorithm in three different settings. Taken together, our contributions represent a concrete step towards black-box application of temporal-difference learning methods in real world problems.

Connected through hyperedge HE-1 (induced by source paper "meta learning state based eligibility traces for more sample efficient policy evaluation"):
  with members:
    - "directly estimating the variance of the lambda return using temporal difference methods"
    - "adaptive lambda least squares temporal difference learning"
    - "true online temporal difference learning"

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 "adaptive temporal difference learning for policy evaluation with per state uncertainty estimates"):
  with members:
    - "temporal difference learning with neural networks study of the leakage propagation problem"
    - "efficient exploration through bayesian deep q networks"
    - "deep exploration via bootstrapped dqn"
    - "adaptive lambda least squares temporal difference learning"
    - "an emphatic approach to the problem of off policy temporal difference learning"
    - "the arcade learning environment an evaluation platform for general agents"
    - "off policy learning with eligibility traces a survey"
    - "should one compute the temporal difference fix point or minimize the bellman residual the unified oblique projection view"

Connected through hyperedge HE-4 (induced by source paper "incrementally learning functions of the return"):
  with members:
    - "directly estimating the variance of the lambda return using temporal difference methods"
    - "reinforcement learning with unsupervised auxiliary tasks"

Connected through hyperedge HE-5 (induced by source paper "meta gradient reinforcement learning"):
  with members:
    - "recasting gradient based meta learning as hierarchical bayes"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "on learning intrinsic rewards for policy gradient methods"
    - "rainbow combining improvements in deep reinforcement learning"
    - "one shot visual imitation learning via meta learning"
    - "a distributional perspective on reinforcement learning"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "meta learning and universality deep representations and gradient descent can approximate any learning algorithm"

Connected through hyperedge HE-6 (induced by source paper "towards autonomous reinforcement learning automatic setting of hyper parameters using bayesian optimization"):
  with members:
    - "generating rescheduling knowledge using reinforcement learning in a cognitive architecture"
    - "bayesian optimization with automatic prior selection for data efficient direct policy search"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "learning to reinforcement learn"
    - "safe multi agent reinforcement learning for autonomous driving"
    - "algorithms for multi armed bandit problems"

Connected through hyperedge HE-7 (induced by source paper "learning to mix n step returns generalizing lambda returns for deep reinforcement learning"):
  with members:
    - "learning to repeat fine grained action repetition for deep reinforcement learning"
    - "the reactor a fast and sample efficient actor critic agent for reinforcement learning"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "asynchronous methods for deep reinforcement learning"
    - "high dimensional continuous control using generalized advantage estimation"
    - "continuous control with deep reinforcement learning"
    - "trust region policy optimization"
    - "the arcade learning environment an evaluation platform for general agents"

Connected through hyperedge HE-8 (induced by source paper "adaptive lambda least squares temporal difference learning"):
  with members:
    - "efficient reinforcement learning using recursive least squares methods"

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.AI(Artificial Intelligence)