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: "supervising strong learners by amplifying weak experts"
  Abstract: Many real world learning tasks involve complex or hard-to-specify objectives, and using an easier-to-specify proxy can lead to poor performance or misaligned behavior. One solution is to have humans provide a training signal by demonstrating or judging performance, but this approach fails if the task is too complicated for a human to directly evaluate. We propose Iterated Amplification, an alternative training strategy which progressively builds up a training signal for difficult problems by combining solutions to easier subproblems. Iterated Amplification is closely related to Expert Iteration (Anthony et al., 2017; Silver et al., 2017), except that it uses no external reward function. We present results in algorithmic environments, showing that Iterated Amplification can efficiently learn complex behaviors.

Connected through hyperedge HE-1 (induced by source paper "ai safety state of the field through quantitative lens"):
  with members:
    - "fairness in learning based sequential decision algorithms a survey"
    - "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"
    - "explainability in human agent systems"
    - "classification schemas for artificial intelligence failures"
    - "explainable artificial intelligence xai concepts taxonomies opportunities and challenges toward responsible ai"
    - "a survey on bias and fairness in machine learning"
    - "machine learning testing survey landscapes and horizons"
    - "unexplainability and incomprehensibility of artificial intelligence"

Connected through hyperedge HE-2 (induced by source paper "fine tuning language models from human preferences"):
  with members:
    - "towards coherent and engaging spoken dialog response generation using automatic conversation evaluators"
    - "finding generalizable evidence by learning to convince q a models"
    - "reward learning for efficient reinforcement learning in extractive document summarisation"
    - "sample efficient text summarization using a single pre trained transformer"
    - "towards coherent and cohesive long form text generation"
    - "deep batch active learning by diverse uncertain gradient lower bounds"
    - "way off policy batch deep reinforcement learning of implicit human preferences in dialog"
    - "learning from dialogue after deployment feed yourself chatbot"

Connected through hyperedge HE-3 (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-4 (induced by source paper "evolutionary computation and ai safety research problems impeding routine and safe real world application of evolution"):
  with members:
    - "paired open ended trailblazer poet endlessly generating increasingly complex and diverse learning environments and their solutions"
    - "agi safety literature review"
    - "born to learn the inspiration progress and future of evolved plastic artificial neural networks"
    - "a study on overfitting in deep reinforcement learning"
    - "scalable agent alignment via reward modeling a research direction"
    - "quantifying generalization in reinforcement learning"
    - "the surprising creativity of digital evolution a collection of anecdotes from the evolutionary computation and artificial life research communities"
    - "incomplete contracting and ai alignment"

Connected through hyperedge HE-5 (induced by source paper "learning human objectives by evaluating hypothetical behavior"):
  with members:
    - "improving safety in reinforcement learning using model based architectures and human intervention"
    - "model based reinforcement learning for atari"
    - "risk aware active inverse reinforcement learning"
    - "learning latent dynamics for planning from pixels"
    - "safe exploration in continuous action spaces"
    - "scalable agent alignment via reward modeling a research direction"
    - "active inverse reward design"
    - "reward learning from human preferences and demonstrations in atari"

Connected through hyperedge HE-6 (induced by source paper "multiparty dynamics and failure modes for machine learning and artificial intelligence"):
  with members:
    - "autocurricula and the emergence of innovation from social interaction a manifesto for multi agent intelligence research"
    - "oversight of unsafe systems via dynamic safety envelopes"
    - "how do classifiers induce agents to invest effort strategically"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "the surprising creativity of digital evolution a collection of anecdotes from the evolutionary computation and artificial life research communities"
    - "robustness to fundamental uncertainty in agi alignment"
    - "data poisoning attacks against online learning"
    - "multi agent reinforcement learning in sequential social dilemmas"

Connected through hyperedge HE-7 (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"
    - "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"
    - "self modification of policy and utility function in rational agents"

Connected through hyperedge HE-8 (induced by source paper "embedded agency"):
  with members:
    - "categorizing variants of goodhart s law"
    - "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"

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.LG(Machine Learning)