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: "q lambda with off policy corrections"
  Abstract: We propose and analyze an alternate approach to off-policy multi-step temporal difference learning, in which off-policy returns are corrected with the current Q-function in terms of rewards, rather than with the target policy in terms of transition probabilities. We prove that such approximate corrections are sufficient for off-policy convergence both in policy evaluation and control, provided certain conditions. These conditions relate the distance between the target and behavior policies, the eligibility trace parameter and the discount factor, and formalize an underlying tradeoff in off-policy TD($\lambda$). We illustrate this theoretical relationship empirically on a continuous-state control task.

Connected through hyperedge HE-1 (induced by source paper "empirical study of off policy policy evaluation for reinforcement learning"):
  with members:
    - "gradientdice rethinking generalized offline estimation of stationary values"
    - "counterfactual off policy evaluation with gumbel max structural causal models"
    - "double reinforcement learning for efficient off policy evaluation in markov decision processes"
    - "off policy evaluation in partially observable environments"
    - "minimax weight and q function learning for off policy evaluation"
    - "off policy policy gradient with state distribution correction"
    - "batch policy learning under constraints"
    - "information theoretic considerations in batch reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "multi step off policy learning without importance sampling ratios"):
  with members:
    - "adaptive lambda least squares temporal difference learning"
    - "data efficient off policy policy evaluation for reinforcement learning"
    - "doubly robust off policy value evaluation for reinforcement learning"
    - "on convergence of emphatic temporal difference learning"
    - "weak convergence properties of constrained emphatic temporal difference learning with constant and slowly diminishing stepsize"

Connected through hyperedge HE-3 (induced by source paper "sample efficient actor critic with experience replay"):
  with members:
    - "asynchronous methods for deep reinforcement learning"
    - "safe and efficient off policy reinforcement learning"
    - "control of memory active perception and action in minecraft"
    - "prioritized experience replay"
    - "high dimensional continuous control using generalized advantage estimation"
    - "continuous control with deep reinforcement learning"
    - "language understanding for text based games using deep reinforcement learning"
    - "trust region policy optimization"

Connected through hyperedge HE-4 (induced by source paper "safe and efficient off policy reinforcement learning"):
  with members:
    - "asynchronous methods for deep reinforcement learning"
    - "prioritized experience replay"
    - "emphatic temporal difference learning"
    - "the arcade learning environment an evaluation platform for general agents"
    - "off policy learning with eligibility traces a survey"

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)