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: "the off switch game"
  Abstract: It is clear that one of the primary tools we can use to mitigate the potential risk from a misbehaving AI system is the ability to turn the system off. As the capabilities of AI systems improve, it is important to ensure that such systems do not adopt subgoals that prevent a human from switching them off. This is a challenge because many formulations of rational agents create strong incentives for self-preservation. This is not caused by a built-in instinct, but because a rational agent will maximize expected utility and cannot achieve whatever objective it has been given if it is dead. Our goal is to study the incentives an agent has to allow itself to be switched off. We analyze a simple game between a human H and a robot R, where H can press R's off switch but R can disable the off switch. A traditional agent takes its reward function for granted: we show that such agents have an incentive to disable the off switch, except in the special case where H is perfectly rational. Our key insight is that for R to want to preserve its off switch, it needs to be uncertain about the utility associated with the outcome, and to treat H's actions as important observations about that utility. (R also has no incentive to switch itself off in this setting.) We conclude that giving machines an appropriate level of uncertainty about their objectives leads to safer designs, and we argue that this setting is a useful generalization of the classical AI paradigm of rational agents.

Connected through hyperedge HE-1 (induced by source paper "reward rational implicit choice a unifying formalism for reward learning"):
  with members:
    - "from language to goals inverse reinforcement learning for vision based instruction following"
    - "preferences implicit in the state of the world"
    - "active inverse reward design"
    - "inverse reward design"
    - "learning preferences for manipulation tasks from online coactive feedback"
    - "generative adversarial imitation learning"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "a joint model of language and perception for grounded attribute learning"

Connected through hyperedge HE-2 (induced by source paper "dave i can assure you that it s going to be all right a definition case for and survey of algorithmic assurances in human autonomy trust relationships"):
  with members:
    - "expressing robot incapability"
    - "uncertainty aware learning from demonstration using mixture density networks with sampling free variance modeling"
    - "towards better analysis of machine learning models a visual analytics perspective"
    - "enabling robots to communicate their objectives"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "challenges for transparency"
    - "combating deep reinforcement learning s sisyphean curse with intrinsic fear"
    - "uncertainty aware reinforcement learning for collision avoidance"

Connected through hyperedge HE-3 (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-4 (induced by source paper "conservative agency via attainable utility preservation"):
  with members:
    - "preferences implicit in the state of the world"
    - "penalizing side effects using stepwise relative reachability"
    - "measuring and avoiding side effects using relative reachability"
    - "a lyapunov based approach to safe reinforcement learning"
    - "categorizing variants of goodhart s law"
    - "incomplete contracting and ai alignment"
    - "ai safety gridworlds"
    - "low impact artificial intelligences"

Connected through hyperedge HE-5 (induced by source paper "optimal farsighted agents tend to seek power"):
  with members:
    - "conservative agency via attainable utility preservation"
    - "a geometric perspective on optimal representations for reinforcement learning"
    - "asymptotically unambitious artificial general intelligence"
    - "empowerment an introduction"

Connected through hyperedge HE-6 (induced by source paper "towards empathetic planning"):
  with members:
    - "emotion in reinforcement learning agents and robots a survey"
    - "back to the future for dialogue research a position paper"
    - "cooperative epistemic multi agent planning for implicit coordination"
    - "plan explanations as model reconciliation moving beyond explanation as soliloquy"

Connected through hyperedge HE-7 (induced by source paper "theories of parenting and their application to artificial intelligence"):
  with members:
    - "the malicious use of artificial intelligence forecasting prevention and mitigation"
    - "semantics derived automatically from language corpora contain human like biases"
    - "concrete problems in ai safety"
    - "do artificial reinforcement learning agents matter morally"

Connected through hyperedge HE-8 (induced by source paper "to monitor or to trust observing robot s behavior based on a game theoretic model of trust"):
  with members:
    - "explicable robot planning as minimizing distance from expected behavior"
    - "plan explicability and predictability for robot task planning"
    - "the fast downward planning system"

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)