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: "accelerated gradient temporal difference learning"
  Abstract: The family of temporal difference (TD) methods span a spectrum from computationally frugal linear methods like TD({\lambda}) to data efficient least squares methods. Least square methods make the best use of available data directly computing the TD solution and thus do not require tuning a typically highly sensitive learning rate parameter, but require quadratic computation and storage. Recent algorithmic developments have yielded several sub-quadratic methods that use an approximation to the least squares TD solution, but incur bias. In this paper, we propose a new family of accelerated gradient TD (ATD) methods that (1) provide similar data efficiency benefits to least-squares methods, at a fraction of the computation and storage (2) significantly reduce parameter sensitivity compared to linear TD methods, and (3) are asymptotically unbiased. We illustrate these claims with a proof of convergence in expectation and experiments on several benchmark domains and a large-scale industrial energy allocation domain.

Connected through hyperedge HE-1 (induced by source paper "kalman meets bellman improving policy evaluation through value tracking"):
  with members:
    - "a kernel loss for solving the bellman equation"
    - "batch kalman normalization towards training deep neural networks with micro batches"
    - "directly estimating the variance of the lambda return using temporal difference methods"
    - "successor uncertainties exploration and uncertainty in temporal difference learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "shallow updates for deep reinforcement learning"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "the uncertainty bellman equation and exploration"

Connected through hyperedge HE-2 (induced by source paper "meta descent for online continual prediction"):
  with members:
    - "on the convergence of adam and beyond"
    - "understanding short horizon bias in stochastic meta optimization"
    - "tidbd adapting temporal difference step sizes through stochastic meta descent"
    - "online learning rate adaptation with hypergradient descent"
    - "effective sketching methods for value function approximation"
    - "learning to learn by gradient descent by gradient descent"
    - "temporal difference networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "decision automation for electric power network recovery"):
  with members:
    - "optimal stochastic dynamic scheduling for managing community recovery from natural hazards"
    - "a modified approximate dynamic programming algorithm for community level food security following disasters"
    - "an approximate dynamic programming approach to food security of communities following hazards"
    - "deep reinforcement learning in large discrete action spaces"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "rollout sampling approximate policy iteration"
    - "approximate policy iteration with a policy language bias solving relational markov decision processes"

Connected through hyperedge HE-4 (induced by source paper "effective sketching methods for value function approximation"):
  with members:
    - "efficient second order online learning by sketching"
    - "unifying task specification in reinforcement learning"
    - "communication optimal distributed principal component analysis in the column partition model"
    - "a practical guide to randomized matrix computations with matlab implementations"
    - "optimal principal component analysis in distributed and streaming models"
    - "on convergence of emphatic temporal difference learning"
    - "sketching as a tool for numerical linear algebra"

Connected through hyperedge HE-5 (induced by source paper "finite sample analyses for td 0 with function approximation"):
  with members:
    - "finite sample analysis of two timescale stochastic approximation with applications to reinforcement learning"
    - "an emphatic approach to the problem of off policy temporal difference learning"
    - "on td 0 with function approximation concentration bounds and a centered variant with exponential convergence"

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)