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: "understanding agent incentives using causal influence diagrams part i single action settings"
  Abstract: Agents are systems that optimize an objective function in an environment. Together, the goal and the environment induce secondary objectives, incentives. Modeling the agent-environment interaction in graphical models called influence diagrams, we can answer two fundamental questions about an agent's incentives directly from the graph: (1) which nodes is the agent incentivized to observe, and (2) which nodes is the agent incentivized to influence? The answers tell us which information and influence points need extra protection. For example, we may want a classifier for job applications to not use the ethnicity of the candidate, and a reinforcement learning agent not to take direct control of its reward mechanism. Different algorithms and training paradigms can lead to different influence diagrams, so our method can be used to identify algorithms with problematic incentives and help in designing algorithms with better incentives.

Connected through hyperedge HE-1 (induced by source paper "resolving spurious correlations in causal models of environments via interventions"):
  with members:
    - "learning causal state representations of partially observable environments"
    - "causal confusion in imitation learning"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "combined reinforcement learning via abstract representations"
    - "woulda coulda shoulda counterfactually guided policy search"
    - "the consciousness prior"
    - "proximal policy optimization algorithms"
    - "concrete problems in ai safety"

Connected through hyperedge HE-2 (induced by source paper "the incentives that shape behaviour"):
  with members:
    - "strategic adaptation to classifiers a causal perspective"
    - "cooperative inverse reinforcement learning"
    - "decision theoretic foundations for causal reasoning"

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"
    - "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"
    - "supervising strong learners by amplifying weak experts"

Connected through hyperedge HE-4 (induced by source paper "on the pitfalls of measuring emergent communication"):
  with members:
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "compositional obverter communication learning from raw visual input"
    - "emergence of communication in an interactive world with consistent speakers"
    - "emergence of linguistic communication from referential games with symbolic and pixel input"
    - "emergent communication through negotiation"
    - "emergent communication in a multi modal multi step referential game"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"

Connected through hyperedge HE-5 (induced by source paper "causal modeling for fairness in dynamical systems"):
  with members:
    - "counterfactual off policy evaluation with gumbel max structural causal models"
    - "the disparate effects of strategic manipulation"
    - "downstream effects of affirmative action"
    - "a short term intervention for long term fairness in the labor market"
    - "the social cost of strategic classification"
    - "woulda coulda shoulda counterfactually guided policy search"
    - "fairness through causal awareness learning latent variable models for biased data"
    - "from fair decision making to social equality"

Connected through hyperedge HE-6 (induced by source paper "modeling agi safety frameworks with causal influence diagrams"):
  with members:
    - "agi safety literature review"
    - "supervising strong learners by amplifying weak experts"
    - "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-7 (induced by source paper "categorizing wireheading in partially embedded agents"):
  with members:
    - "embedded agency"
    - "strong asymptotic optimality in general environments"
    - "ai safety gridworlds"
    - "aixijs a software demo for general reinforcement learning"
    - "universal reinforcement learning algorithms survey and experiments"
    - "generalised discount functions applied to a monte carlo aimu implementation"
    - "avoiding wireheading with value reinforcement learning"
    - "model based utility functions"

Connected through hyperedge HE-8 (induced by source paper "strategic classification is causal modeling in disguise"):
  with members:
    - "optimal decision making under strategic behavior"
    - "the disparate effects of strategic manipulation"
    - "how do classifiers induce agents to invest effort strategically"
    - "the social cost of strategic classification"
    - "strategic classification from revealed preferences"
    - "on the number of experiments sufficient and in the worst case necessary to identify all causal relations among n variables"

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)