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: "provably bounded optimal agents"
  Abstract: Since its inception, artificial intelligence has relied upon a theoretical foundation centered around perfect rationality as the desired property of intelligent systems. We argue, as others have done, that this foundation is inadequate because it imposes fundamentally unsatisfiable requirements. As a result, there has arisen a wide gap between theory and practice in AI, hindering progress in the field. We propose instead a property called bounded optimality. Roughly speaking, an agent is bounded-optimal if its program is a solution to the constrained optimization problem presented by its architecture and the task environment. We show how to construct agents with this property for a simple class of machine architectures in a broad class of real-time environments. We illustrate these results using a simple model of an automated mail sorting facility. We also define a weaker property, asymptotic bounded optimality (ABO), that generalizes the notion of optimality in classical complexity theory. We then construct universal ABO programs, i.e., programs that are ABO no matter what real-time constraints are applied. Universal ABO programs can be used as building blocks for more complex systems. We conclude with a discussion of the prospects for bounded optimality as a theoretical basis for AI, and relate it to similar trends in philosophy, economics, and game theory.

Connected through hyperedge HE-1 (induced by source paper "the efficiency of human cognition reflects planned information processing"):
  with members:
    - "adam a method for stochastic optimization"
    - "on the complexity of solving markov decision problems"
    - "hierarchical reinforcement learning with the maxq value function decomposition"

Connected through hyperedge HE-2 (induced by source paper "density encoding enables resource efficient randomly connected neural networks"):
  with members:
    - "a theory of sequence indexing and working memory in recurrent neural networks"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "the high dimensional geometry of binary neural networks"
    - "bitwise neural networks"
    - "ternary neural networks for resource efficient ai applications"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "representing objects relations and sequences"

Connected through hyperedge HE-3 (induced by source paper "bounded rational decision making from elementary computations that reduce uncertainty"):
  with members:
    - "systems of bounded rational agents with information theoretic constraints"
    - "bounded rational decision making with adaptive neural network priors"
    - "a short characterization of relative entropy"
    - "regularised deep reinforcement learning with guaranteed convergence"
    - "an information theoretic optimality principle for deep reinforcement learning"
    - "information measures the curious case of the binary alphabet"
    - "a minimum relative entropy principle for learning and acting"

Connected through hyperedge HE-4 (induced by source paper "systems of bounded rational agents with information theoretic constraints"):
  with members:
    - "a constraint handling technique for genetic algorithms using a violation factor"
    - "information utility bounded rationality"

Connected through hyperedge HE-5 (induced by source paper "learning to select computations"):
  with members:
    - "learning to reinforcement learn"
    - "building machines that learn and think like people"
    - "metareasoning for planning under uncertainty"
    - "selecting computations theory and applications"

Connected through hyperedge HE-6 (induced by source paper "research priorities for robust and beneficial artificial intelligence"):
  with members:
    - "toward idealized decision theory"
    - "friendly artificial intelligence the physics challenge"
    - "practical verification of decision making in agent based autonomous systems"
    - "decision theory with resource bounded agents"
    - "game theory with translucent players"
    - "reasoning about beliefs and actions under computational resource constraints"
    - "quasi newton methods a new direction"
    - "i don t want to think about it now decision theory with costly computation"

Connected through hyperedge HE-7 (induced by source paper "bounded optimal exploration in mdp"):
  with members:
    - "a greedy approximation of bayesian reinforcement learning with probably optimistic transition model"
    - "incremental model based learners with formal learning time guarantees"
    - "near optimal brl using optimistic local transitions"
    - "variance based rewards for approximate bayesian reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "generalized thompson sampling for sequential decision making and causal inference"):
  with members:
    - "learning to optimize via posterior sampling"
    - "monte carlo utility estimates for bayesian reinforcement learning"
    - "thompson sampling in switching environments with bayesian online change point detection"
    - "a bayesian sampling approach to exploration in reinforcement learning"
    - "information utility bounded rationality"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "a bayesian rule for adaptive control based on causal interventions"
    - "a minimum relative entropy principle for learning and acting"

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)