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: "occam s razor is insufficient to infer the preferences of irrational agents"
  Abstract: Inverse reinforcement learning (IRL) attempts to infer human rewards or preferences from observed behavior. Since human planning systematically deviates from rationality, several approaches have been tried to account for specific human shortcomings. However, the general problem of inferring the reward function of an agent of unknown rationality has received little attention. Unlike the well-known ambiguity problems in IRL, this one is practically relevant but cannot be resolved by observing the agent's policy in enough environments. This paper shows (1) that a No Free Lunch result implies it is impossible to uniquely decompose a policy into a planning algorithm and reward function, and (2) that even with a reasonable simplicity prior/Occam's razor on the set of decompositions, we cannot distinguish between the true decomposition and others that lead to high regret. To address this, we need simple `normative' assumptions, which cannot be deduced exclusively from observations.

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 "better than demonstrator imitation learning via automatically ranked demonstrations"):
  with members:
    - "inverse reinforcement learning with multiple ranked experts"
    - "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations"
    - "imitation learning from imperfect demonstration"
    - "a review of robot learning for manipulation challenges representations and algorithms"
    - "risk aware active inverse reinforcement learning"
    - "reinforcement learning from imperfect demonstrations"
    - "an algorithmic perspective on imitation learning"
    - "ensembledagger a bayesian approach to safe imitation learning"

Connected through hyperedge HE-3 (induced by source paper "on the feasibility of learning rather than assuming human biases for reward inference"):
  with members:
    - "universal planning networks"
    - "multi task maximum entropy inverse reinforcement learning"
    - "learning to search with mctsnets"
    - "learning a prior over intent via meta inverse reinforcement learning"
    - "machine theory of mind"
    - "where do you think you re going inferring beliefs about dynamics from behavior"
    - "learning model based planning from scratch"
    - "inverse reward design"

Connected through hyperedge HE-4 (induced by source paper "embedded agency"):
  with members:
    - "supervising strong learners by amplifying weak experts"
    - "categorizing variants of goodhart s law"
    - "functional decision theory a new theory of instrumental rationality"
    - "reinforcement learning with a corrupted reward channel"
    - "cooperative inverse reinforcement learning"
    - "nonparametric general reinforcement learning"
    - "concrete problems in ai safety"
    - "a formal solution to the grain of truth problem"

Connected through hyperedge HE-5 (induced by source paper "risks from learned optimization in advanced machine learning systems"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "universal planning networks"
    - "scalable agent alignment via reward modeling a research direction"
    - "treeqn and atreec differentiable tree structured models for deep reinforcement learning"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "learning to reinforcement learn"
    - "learning to learn by gradient descent by gradient descent"
    - "safety verification of deep neural networks"

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)