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: "self modification of policy and utility function in rational agents"
  Abstract: Any agent that is part of the environment it interacts with and has versatile actuators (such as arms and fingers), will in principle have the ability to self-modify -- for example by changing its own source code. As we continue to create more and more intelligent agents, chances increase that they will learn about this ability. The question is: will they want to use it? For example, highly intelligent systems may find ways to change their goals to something more easily achievable, thereby `escaping' the control of their designers. In an important paper, Omohundro (2008) argued that goal preservation is a fundamental drive of any intelligent system, since a goal is more likely to be achieved if future versions of the agent strive towards the same goal. In this paper, we formalise this argument in general reinforcement learning, and explore situations where it fails. Our conclusion is that the self-modification possibility is harmless if and only if the value function of the agent anticipates the consequences of self-modifications and use the current utility function when evaluating the future.

Connected through hyperedge HE-1 (induced by source paper "reward tampering problems and solutions in reinforcement learning a causal influence diagram perspective"):
  with members:
    - "categorizing wireheading in partially embedded agents"
    - "model based reinforcement learning for atari"
    - "understanding agent incentives using causal influence diagrams part i single action settings"
    - "modeling agi safety frameworks with causal influence diagrams"
    - "neural predictive belief representations"
    - "agi safety literature review"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "world models"

Connected through hyperedge HE-2 (induced by source paper "understanding agent incentives using causal influence diagrams part i single action settings"):
  with members:
    - "modeling agi safety frameworks with causal influence diagrams"
    - "agi safety literature review"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "the surprising creativity of digital evolution a collection of anecdotes from the evolutionary computation and artificial life research communities"
    - "ai safety gridworlds"
    - "on formalizing fairness in prediction with machine learning"
    - "a game theoretic analysis of the off switch game"
    - "good and safe uses of ai oracles"

Connected through hyperedge HE-3 (induced by source paper "modeling agi safety frameworks with causal influence diagrams"):
  with members:
    - "understanding agent incentives using causal influence diagrams part i single action settings"
    - "agi safety literature review"
    - "supervising strong learners by amplifying weak experts"
    - "scalable agent alignment via reward modeling a research direction"
    - "agents and devices a relative definition of agency"
    - "good and safe uses of ai oracles"
    - "learning to reinforcement learn"
    - "concrete problems in ai safety"

Connected through hyperedge HE-4 (induced by source paper "corrigibility with utility preservation"):
  with members:
    - "agi safety literature review"
    - "ai safety gridworlds"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "incorrigibility in the cirl framework"
    - "universal algorithmic intelligence a mathematical top down approach"

Connected through hyperedge HE-5 (induced by source paper "ai safety gridworlds"):
  with members:
    - "rainbow combining improvements in deep reinforcement learning"
    - "adversarial attacks on neural network policies"
    - "interactive learning from policy dependent human feedback"
    - "low impact artificial intelligences"
    - "towards proving the adversarial robustness of deep neural networks"
    - "a distributional perspective on reinforcement learning"
    - "enter the matrix safely interruptible autonomous systems via virtualization"
    - "should robots be obedient"

Connected through hyperedge HE-6 (induced by source paper "indifference methods for managing agent rewards"):
  with members:
    - "counterfactual equivalence for pomdps and underlying deterministic environments"
    - "agi safety literature review"
    - "ai safety gridworlds"
    - "should robots be obedient"
    - "good and safe uses of ai oracles"
    - "concrete problems in ai safety"
    - "can intelligence explode"

Connected through hyperedge HE-7 (induced by source paper "a game theoretic analysis of the off switch game"):
  with members:
    - "the off switch game"
    - "death and suicide in universal artificial intelligence"

Connected through hyperedge HE-8 (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"
    - "parametric bounded lob s theorem and robust cooperation of bounded agents"
    - "guided cost learning deep inverse optimal control via policy optimization"

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)