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: "convergent tree backup and retrace with function approximation"
  Abstract: Off-policy learning is key to scaling up reinforcement learning as it allows to learn about a target policy from the experience generated by a different behavior policy. Unfortunately, it has been challenging to combine off-policy learning with function approximation and multi-step bootstrapping in a way that leads to both stable and efficient algorithms. In this paper, we show that the Tree Backup and Retrace algorithms are unstable with linear function approximation, both in theory and with specific examples. Based on our analysis, we then derive stable and efficient gradient-based algorithms, compatible with accumulating or Dutch traces, using a novel methodology based on saddle-point methods. In addition to convergence guarantees, we provide finite-sample analysis.

Connected through hyperedge HE-1 (induced by source paper "ranking policy gradient"):
  with members:
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "if maxent rl is the answer what is the question"
    - "dopamine a research framework for deep reinforcement learning"
    - "self imitation learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "implicit quantile networks for distributional reinforcement learning"
    - "variational bayesian reinforcement learning with regret bounds"
    - "are deep policy gradient algorithms truly policy gradient algorithms"

Connected through hyperedge HE-2 (induced by source paper "neural temporal difference learning converges to global optima"):
  with members:
    - "finite sample analysis for sarsa and q learning with linear function approximation"
    - "finite time error bounds for linear stochastic approximation and td learning"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "a finite time analysis of temporal difference learning with linear function approximation"
    - "feature based aggregation and deep reinforcement learning a survey and some new implementations"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "td or not td analyzing the role of temporal differencing in deep reinforcement learning"
    - "towards understanding the role of over parametrization in generalization of neural networks"

Connected through hyperedge HE-3 (induced by source paper "modified actor critics"):
  with members:
    - "deep conservative policy iteration"
    - "a theory of regularized markov decision processes"
    - "soft actor critic algorithms and applications"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "relative entropy regularized policy iteration"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "reinforcement learning with deep energy based policies"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-4 (induced by source paper "expected sarsa λ with control variate for variance reduction"):
  with members:
    - "per decision multi step temporal difference learning with control variates"
    - "finite sample analysis of the gtd policy evaluation algorithms in markov setting"
    - "finite sample analysis of two timescale stochastic approximation with applications to reinforcement learning"
    - "multi step off policy learning without importance sampling ratios"
    - "finite sample analyses for td 0 with function approximation"
    - "investigating practical linear temporal difference learning"
    - "an emphatic approach to the problem of off policy temporal difference learning"
    - "on convergence of emphatic temporal difference learning"

Connected through hyperedge HE-5 (induced by source paper "finite sample analysis for sarsa and q learning with linear function approximation"):
  with members:
    - "a finite time analysis of temporal difference learning with linear function approximation"
    - "differential temporal difference learning"
    - "finite sample analysis for decentralized batch multi agent reinforcement learning with networked agents"
    - "least squares temporal difference learning for the linear quadratic regulator"
    - "asynchronous methods for deep reinforcement learning"
    - "a simpler approach to obtaining an o 1 t convergence rate for the projected stochastic subgradient method"

Connected through hyperedge HE-6 (induced by source paper "svrg for policy evaluation with fewer gradient evaluations"):
  with members:
    - "stochastic variance reduced policy gradient"
    - "stochastic variance reduction methods for policy evaluation"
    - "stochastic variance reduction methods for saddle point problems"
    - "stop wasting my gradients practical svrg"
    - "on td 0 with function approximation concentration bounds and a centered variant with exponential convergence"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Connected through hyperedge HE-7 (induced by source paper "gradient q sigma lambda a unified algorithm with function approximation for reinforcement learning"):
  with members:
    - "a unified approach for multi step temporal difference learning with eligibility traces in reinforcement learning"
    - "an emphatic approach to the problem of off policy temporal difference learning"

Connected through hyperedge HE-8 (induced by source paper "maximum a posteriori policy optimisation"):
  with members:
    - "equivalence between policy gradients and soft q learning"
    - "hindsight experience replay"
    - "a distributional perspective on reinforcement learning"
    - "emergence of locomotion behaviours in rich environments"
    - "stochastic neural networks for hierarchical reinforcement learning"
    - "generative adversarial imitation learning"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"

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)