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: "time consistent discounting"
  Abstract: A possibly immortal agent tries to maximise its summed discounted rewards over time, where discounting is used to avoid infinite utilities and encourage the agent to value current rewards more than future ones. Some commonly used discount functions lead to time-inconsistent behavior where the agent changes its plan over time. These inconsistencies can lead to very poor behavior. We generalise the usual discounted utility model to one where the discount function changes with the age of the agent. We then give a simple characterisation of time-(in)consistent discount functions and show the existence of a rational policy for an agent that knows its discount function is time-inconsistent.

Connected through hyperedge HE-1 (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-2 (induced by source paper "the sample complexity of general reinforcement learning"):
  with members:
    - "optimal regret bounds for selecting the state representation in reinforcement learning"
    - "on the sample complexity of reinforcement learning with a generative model"
    - "pac bounds for discounted mdps"
    - "optimistic agents are asymptotically optimal"
    - "asymptotically optimal agents"
    - "on the possibility of learning in reactive environments with arbitrary dependence"
    - "self optimizing and pareto optimal policies in general environments based on bayes mixtures"

Connected through hyperedge HE-3 (induced by source paper "optimistic agents are asymptotically optimal"):
  with members:
    - "selecting the state representation in reinforcement learning"
    - "pac bounds for discounted mdps"
    - "asymptotically optimal agents"
    - "on the possibility of learning in reactive environments with arbitrary dependence"

Connected through hyperedge HE-4 (induced by source paper "asymptotically optimal agents"):
  with members:
    - "on the possibility of learning in reactive environments with arbitrary dependence"
    - "universal intelligence a definition of machine intelligence"
    - "is there an elegant universal theory of prediction"
    - "self optimizing and pareto optimal policies in general environments based on bayes mixtures"

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)