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: "incomplete contracting and ai alignment"
  Abstract: We suggest that the analysis of incomplete contracting developed by law and economics researchers can provide a useful framework for understanding the AI alignment problem and help to generate a systematic approach to finding solutions. We first provide an overview of the incomplete contracting literature and explore parallels between this work and the problem of AI alignment. As we emphasize, misalignment between principal and agent is a core focus of economic analysis. We highlight some technical results from the economics literature on incomplete contracts that may provide insights for AI alignment researchers. Our core contribution, however, is to bring to bear an insight that economists have been urged to absorb from legal scholars and other behavioral scientists: the fact that human contracting is supported by substantial amounts of external structure, such as generally available institutions (culture, law) that can supply implied terms to fill the gaps in incomplete contracts. We propose a research agenda for AI alignment work that focuses on the problem of how to build AI that can replicate the human cognitive processes that connect individual incomplete contracts with this supporting external structure.

Connected through hyperedge HE-1 (induced by source paper "artificial intelligence values and alignment"):
  with members:
    - "agi safety literature review"
    - "scalable agent alignment via reward modeling a research direction"
    - "machine theory of mind"
    - "impossibility and uncertainty theorems in ai value alignment or why your agi should not have a utility function"
    - "constrained policy optimization"
    - "universal intelligence a definition of machine intelligence"

Connected through hyperedge HE-2 (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"
    - "supervising strong learners by amplifying weak experts"
    - "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"

Connected through hyperedge HE-3 (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"
    - "ai safety gridworlds"
    - "low impact artificial intelligences"
    - "leave no trace learning to reset for safe and autonomous reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "learning from learning machines optimisation rules and social norms"):
  with members:
    - "deep learning vs traditional computer vision"
    - "tensor2tensor for neural machine translation"
    - "recent trends in deep learning based natural language processing"
    - "a survey on deep learning in medical image analysis"
    - "deep learning in bioinformatics"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "deep image scaling up image recognition"
    - "deep multimodal learning for audio visual speech recognition"

Connected through hyperedge HE-5 (induced by source paper "hard choices in artificial intelligence addressing normative uncertainty through sociotechnical commitments"):
  with members:
    - "data science as political action grounding data science in a politics of justice"
    - "impossibility and uncertainty theorems in ai value alignment or why your agi should not have a utility function"

Connected through hyperedge HE-6 (induced by source paper "teaching social behavior through human reinforcement for ad hoc teamwork the star framework"):
  with members:
    - "reasoning about hypothetical agent behaviours and their parameters"
    - "incorporating behavioral constraints in online ai systems"
    - "building ethically bounded ai"
    - "building ethics into artificial intelligence"
    - "inverse reward design"
    - "safe reinforcement learning via shielding"
    - "belief and truth in hypothesised behaviours"
    - "a game theoretic model and best response learning method for ad hoc coordination in multiagent systems"

Connected through hyperedge HE-7 (induced by source paper "self regulating artificial general intelligence"):
  with members:
    - "concrete problems in ai safety"
    - "the off switch game"

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)