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: "universal intelligence a definition of machine intelligence"
  Abstract: A fundamental problem in artificial intelligence is that nobody really knows what intelligence is. The problem is especially acute when we need to consider artificial systems which are significantly different to humans. In this paper we approach this problem in the following way: We take a number of well known informal definitions of human intelligence that have been given by experts, and extract their essential features. These are then mathematically formalised to produce a general measure of intelligence for arbitrary machines. We believe that this equation formally captures the concept of machine intelligence in the broadest reasonable sense. We then show how this formal definition is related to the theory of universal optimal learning agents. Finally, we survey the many other tests and definitions of intelligence that have been proposed for machines.

Connected through hyperedge HE-1 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning"):
  with members:
    - "learning representations for neural network based classification using the information bottleneck principle"
    - "dynamics aware unsupervised discovery of skills"
    - "causal confusion in imitation learning"
    - "unsupervised discovery of temporal structure in noisy data with dynamical components analysis"
    - "learnability for the information bottleneck"
    - "pareto optimal data compression for binary classification tasks"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "the conditional entropy bottleneck"

Connected through hyperedge HE-2 (induced by source paper "artificial intelligence values and alignment"):
  with members:
    - "agi safety literature review"
    - "scalable agent alignment via reward modeling a research direction"
    - "incomplete contracting and ai alignment"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "unmasking clever hans predictors and assessing what machines really learn"):
  with members:
    - "deep neural networks for no reference and full reference image quality assessment"
    - "methods for interpreting and understanding deep neural networks"
    - "explaining the unique nature of individual gait patterns with deep learning"
    - "explaining recurrent neural network predictions in sentiment analysis"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "visualizing and understanding atari agents"
    - "what is relevant in a text document an interpretable machine learning approach"
    - "socially aware motion planning with deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "meta learning of sequential strategies"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "on first order meta learning algorithms"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "reptile a scalable metalearning algorithm"
    - "causal reasoning from meta reinforcement learning"
    - "conditional neural processes"
    - "sample efficient adaptive text to speech"
    - "modeling friends and foes"

Connected through hyperedge HE-5 (induced by source paper "on the measure of intelligence"):
  with members:
    - "the minerl competition on sample efficient reinforcement learning using human priors"
    - "behaviour suite for reinforcement learning"
    - "paired open ended trailblazer poet endlessly generating increasingly complex and diverse learning environments and their solutions"
    - "obstacle tower a generalization challenge in vision control and planning"
    - "superglue a stickier benchmark for general purpose language understanding systems"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "sample efficient reinforcement learning with stochastic ensemble value expansion"
    - "quantifying generalization in reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "understanding agent incentives using causal influence diagrams part i single action settings"):
  with members:
    - "modeling agi safety frameworks with causal influence diagrams"
    - "agi safety literature review"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "the surprising creativity of digital evolution a collection of anecdotes from the evolutionary computation and artificial life research communities"
    - "ai safety gridworlds"
    - "on formalizing fairness in prediction with machine learning"
    - "a game theoretic analysis of the off switch game"
    - "good and safe uses of ai oracles"

Connected through hyperedge HE-7 (induced by source paper "vrgym a virtual testbed for physical and interactive ai"):
  with members:
    - "configurable 3d scene synthesis and 2d image rendering with per pixel ground truth using stochastic grammars"
    - "gibson env real world perception for embodied agents"
    - "human centric indoor scene synthesis using stochastic grammar"
    - "predicting human activities using stochastic grammar"
    - "learning social affordance grammar from videos transferring human interactions to human robot interactions"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "ai2 thor an interactive 3d environment for visual ai"

Connected through hyperedge HE-8 (induced by source paper "efficient continual learning in neural networks with embedding regularization"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "don t forget there is more than forgetting new metrics for continual learning"
    - "rotate your networks better weight consolidation and less catastrophic forgetting"
    - "efficient lifelong learning with a gem"
    - "pseudo recursal solving the catastrophic forgetting problem in deep neural networks"
    - "progressive neural networks"
    - "neural graph machines learning neural networks using graphs"
    - "deep image prior"

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)