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: "parametric bounded lob s theorem and robust cooperation of bounded agents"
  Abstract: L\"ob's theorem and G\"odel's theorems make predictions about the behavior of systems capable of self-reference with unbounded computational resources with which to write and evaluate proofs. However, in the real world, systems capable of self-reference will have limited memory and processing speed, so in this paper we introduce an effective version of L\"ob's theorem which is applicable given such bounded resources. These results have powerful implications for the game theory of bounded agents who are able to write proofs about themselves and one another, including the capacity to out-perform classical Nash equilibria and correlated equilibria, attaining mutually cooperative program equilibrium in the Prisoner's Dilemma. Previous cooperative program equilibria studied by Tennenholtz (2004) and Fortnow (2009) have depended on tests for program equality, a fragile condition, whereas "L\"obian" cooperation is much more robust and agnostic of the opponent's implementation.

Connected through hyperedge HE-1 (induced by source paper "toward negotiable reinforcement learning shifting priorities in pareto optimal sequential decision making"):
  with members:
    - "cooperative inverse reinforcement learning"
    - "toward idealized decision theory"

Connected through hyperedge HE-2 (induced by source paper "concrete problems in ai safety"):
  with members:
    - "safe exploration in finite markov decision processes with gaussian processes"
    - "practical black box attacks against machine learning"
    - "deep exploration via bootstrapped dqn"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "asymptotic convergence in online learning with unbounded delays"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "avoiding wireheading with value reinforcement learning"

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.GT(Computer Science and Game Theory)