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: "categorizing variants of goodhart s law"
  Abstract: There are several distinct failure modes for overoptimization of systems on the basis of metrics. This occurs when a metric which can be used to improve a system is used to an extent that further optimization is ineffective or harmful, and is sometimes termed Goodhart's Law. This class of failure is often poorly understood, partly because terminology for discussing them is ambiguous, and partly because discussion using this ambiguous terminology ignores distinctions between different failure modes of this general type. This paper expands on an earlier discussion by Garrabrant, which notes there are "(at least) four different mechanisms" that relate to Goodhart's Law. This paper is intended to explore these mechanisms further, and specify more clearly how they occur. This discussion should be helpful in better understanding these types of failures in economic regulation, in public policy, in machine learning, and in Artificial Intelligence alignment. The importance of Goodhart effects depends on the amount of power directed towards optimizing the proxy, and so the increased optimization power offered by artificial intelligence makes it especially critical for that field.

Connected through hyperedge HE-1 (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"
    - "incomplete contracting and ai alignment"
    - "ai safety gridworlds"
    - "low impact artificial intelligences"
    - "leave no trace learning to reset for safe and autonomous reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "personal universes a solution to the multi agent value alignment problem"):
  with members:
    - "the malicious use of artificial intelligence forecasting prevention and mitigation"
    - "ai safety and reproducibility establishing robust foundations for the neuropsychology of human values"
    - "agi safety literature review"
    - "uploading brain into computer whom to upload first"
    - "building safer agi by introducing artificial stupidity"
    - "mimetic vs anchored value alignment in artificial intelligence"
    - "on the origin of synthetic life attribution of output to a particular algorithm"
    - "detecting qualia in natural and artificial agents"

Connected through hyperedge HE-3 (induced by source paper "embedded agency"):
  with members:
    - "supervising strong learners by amplifying weak experts"
    - "occam s razor is insufficient to infer the preferences of irrational agents"
    - "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-4 (induced by source paper "krnc new foundations for permissionless byzantine consensus and global monetary stability"):
  with members:
    - "overoptimization failures and specification gaming in multi agent systems"

Connected through hyperedge HE-5 (induced by source paper "agi safety literature review"):
  with members:
    - "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"
    - "should robots be obedient"

Connected through hyperedge HE-6 (induced by source paper "overoptimization failures and specification gaming in multi agent systems"):
  with members:
    - "oversight of unsafe systems via dynamic safety envelopes"
    - "supervising strong learners by amplifying weak experts"
    - "data poisoning attacks against online learning"
    - "on a formal model of safe and scalable self driving cars"
    - "targeted backdoor attacks on deep learning systems using data poisoning"
    - "models and framework for adversarial attacks on complex adaptive systems"
    - "concrete problems in ai safety"
    - "stealing machine learning models via prediction apis"

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)