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: "a collection of definitions of intelligence"
  Abstract: This paper is a survey of a large number of informal definitions of ``intelligence'' that the authors have collected over the years. Naturally, compiling a complete list would be impossible as many definitions of intelligence are buried deep inside articles and books. Nevertheless, the 70-odd definitions presented here are, to the authors' knowledge, the largest and most well referenced collection there is.

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 "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-3 (induced by source paper "tackling partial domain adaptation with self supervision"):
  with members:
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "domain generalization by solving jigsaw puzzles"
    - "boosting domain adaptation by discovering latent domains"
    - "importance weighted adversarial nets for partial domain adaptation"
    - "partial adversarial domain adaptation"
    - "adaptive deep learning through visual domain localization"
    - "deepjdot deep joint distribution optimal transport for unsupervised domain adaptation"
    - "twins two weighted inconsistency reduced networks for partial domain adaptation"

Connected through hyperedge HE-4 (induced by source paper "behaviour suite for reinforcement learning"):
  with members:
    - "benchmarking model based reinforcement learning"
    - "distributed prioritized experience replay"
    - "deep reinforcement learning that matters"
    - "spectrally normalized margin bounds for neural networks"
    - "revisiting the arcade learning environment evaluation protocols and open problems for general agents"
    - "deep exploration via bootstrapped dqn"
    - "benchmarking deep reinforcement learning for continuous control"
    - "massively parallel methods for deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "classification schemas for artificial intelligence failures"):
  with members:
    - "unpredictability of ai"
    - "the surprising creativity of digital evolution a collection of anecdotes from the evolutionary computation and artificial life research communities"
    - "semantics derived automatically from language corpora contain human like biases"
    - "adverse events in robotic surgery a retrospective study of 14 years of fda data"
    - "artificial intelligence safety and cybersecurity a timeline of ai failures"
    - "taxonomy of pathways to dangerous ai"

Connected through hyperedge HE-6 (induced by source paper "towards self constructive artificial intelligence algorithmic basis part i"):
  with members:
    - "dynamic routing between capsules"
    - "towards the self constructive brain emergence of adaptive behavior"

Connected through hyperedge HE-7 (induced by source paper "a new framework for machine intelligence concepts and prototype"):
  with members:
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "data efficient hierarchical reinforcement learning"
    - "visual interpretability for deep learning a survey"
    - "adversarial patch"
    - "world models"
    - "deep learning a critical appraisal"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "dynamic routing between capsules"

Connected through hyperedge HE-8 (induced by source paper "fractal ai a fragile theory of intelligence"):
  with members:
    - "noisy networks for exploration"
    - "empowerment an introduction"

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)