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: "should robots be obedient"
  Abstract: Intuitively, obedience -- following the order that a human gives -- seems like a good property for a robot to have. But, we humans are not perfect and we may give orders that are not best aligned to our preferences. We show that when a human is not perfectly rational then a robot that tries to infer and act according to the human's underlying preferences can always perform better than a robot that simply follows the human's literal order. Thus, there is a tradeoff between the obedience of a robot and the value it can attain for its owner. We investigate how this tradeoff is impacted by the way the robot infers the human's preferences, showing that some methods err more on the side of obedience than others. We then analyze how performance degrades when the robot has a misspecified model of the features that the human cares about or the level of rationality of the human. Finally, we study how robots can start detecting such model misspecification. Overall, our work suggests that there might be a middle ground in which robots intelligently decide when to obey human orders, but err on the side of obedience.

Connected through hyperedge HE-1 (induced by source paper "safe imitation learning via fast bayesian reward inference from preferences"):
  with members:
    - "pragmatic pedagogic value alignment"
    - "asking easy questions a user friendly approach to active reward learning"
    - "extending deep model predictive control with safety augmented value estimation from demonstrations"
    - "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations"
    - "functional variational bayesian neural networks"
    - "causal confusion in imitation learning"
    - "a divergence minimization perspective on imitation learning methods"
    - "a simple baseline for bayesian uncertainty in deep learning"

Connected through hyperedge HE-2 (induced by source paper "safelife 1 0 exploring side effects in complex environments"):
  with members:
    - "preferences implicit in the state of the world"
    - "penalizing side effects using stepwise relative reachability"
    - "scalable agent alignment via reward modeling a research direction"
    - "leave no trace learning to reset for safe and autonomous reinforcement learning"
    - "proximal policy optimization algorithms"
    - "learning to reinforcement learn"
    - "squad 100 000 questions for machine comprehension of text"
    - "concrete problems in ai safety"

Connected through hyperedge HE-3 (induced by source paper "agi safety literature review"):
  with members:
    - "categorizing variants of goodhart s law"
    - "psychlab a psychology laboratory for deep reinforcement learning agents"
    - "rainbow combining improvements in deep reinforcement learning"
    - "ai safety gridworlds"
    - "analyzing hidden representations in end to end automatic speech recognition systems"
    - "a causal framework for explaining the predictions of black box sequence to sequence models"
    - "when will ai exceed human performance evidence from ai experts"
    - "deep tamer interactive agent shaping in high dimensional state spaces"

Connected through hyperedge HE-4 (induced by source paper "machine theory of mind"):
  with members:
    - "pragmatic pedagogic value alignment"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "robust imitation of diverse behaviors"
    - "meta learning and universality deep representations and gradient descent can approximate any learning algorithm"
    - "building machines that learn and think for themselves commentary on lake et al behavioral and brain sciences 2017"
    - "one shot imitation learning"
    - "deep variational information bottleneck"
    - "generative adversarial imitation learning"

Connected through hyperedge HE-5 (induced by source paper "learning under misspecified objective spaces"):
  with members:
    - "an algorithmic perspective on imitation learning"
    - "variational inverse control with events a general framework for data driven reward definition"
    - "inverse reward design"
    - "learning preferences for manipulation tasks from online coactive feedback"
    - "shared autonomy via hindsight optimization"

Connected through hyperedge HE-6 (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"
    - "constrained policy optimization"

Connected through hyperedge HE-7 (induced by source paper "occam s razor is insufficient to infer the preferences of irrational agents"):
  with members:
    - "exploring hierarchy aware inverse reinforcement learning"
    - "reinforcement learning with a corrupted reward channel"
    - "inverse reward design"
    - "towards resolving unidentifiability in inverse reinforcement learning"
    - "cooperative inverse reinforcement learning"
    - "avoiding wireheading with value reinforcement learning"
    - "research priorities for robust and beneficial artificial intelligence"

Connected through hyperedge HE-8 (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"
    - "good and safe uses of ai oracles"
    - "concrete problems in ai safety"
    - "self modification of policy and utility function in rational agents"
    - "can intelligence explode"

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)