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: "on the measure of intelligence"
  Abstract: To make deliberate progress towards more intelligent and more human-like artificial systems, we need to be following an appropriate feedback signal: we need to be able to define and evaluate intelligence in a way that enables comparisons between two systems, as well as comparisons with humans. Over the past hundred years, there has been an abundance of attempts to define and measure intelligence, across both the fields of psychology and AI. We summarize and critically assess these definitions and evaluation approaches, while making apparent the two historical conceptions of intelligence that have implicitly guided them. We note that in practice, the contemporary AI community still gravitates towards benchmarking intelligence by comparing the skill exhibited by AIs and humans at specific tasks such as board games and video games. We argue that solely measuring skill at any given task falls short of measuring intelligence, because skill is heavily modulated by prior knowledge and experience: unlimited priors or unlimited training data allow experimenters to "buy" arbitrary levels of skills for a system, in a way that masks the system's own generalization power. We then articulate a new formal definition of intelligence based on Algorithmic Information Theory, describing intelligence as skill-acquisition efficiency and highlighting the concepts of scope, generalization difficulty, priors, and experience. Using this definition, we propose a set of guidelines for what a general AI benchmark should look like. Finally, we present a benchmark closely following these guidelines, the Abstraction and Reasoning Corpus (ARC), built upon an explicit set of priors designed to be as close as possible to innate human priors. We argue that ARC can be used to measure a human-like form of general fluid intelligence and that it enables fair general intelligence comparisons between AI systems and humans.

Connected through hyperedge HE-1 (induced by source paper "the next decade in ai four steps towards robust artificial intelligence"):
  with members:
    - "memo a deep network for flexible combination of episodic memories"
    - "towards a human like open domain chatbot"
    - "learning higher order logic programs"
    - "solving rubik s cube with a robot hand"
    - "genesis generative scene inference and sampling with object centric latent representations"
    - "entity abstraction in visual model based reinforcement learning"
    - "emergent systematic generalization in a situated agent"
    - "monet unsupervised scene decomposition and representation"

Connected through hyperedge HE-2 (induced by source paper "turning 30 new ideas in inductive logic programming"):
  with members:
    - "learning higher order logic programs"
    - "neural logic machines"
    - "logical reduction of metarules"
    - "forgetting to learn logic programs"
    - "playgol learning programs through play"
    - "synthesizing datalog programs using numerical relaxation"
    - "learning relational representations with auto encoding logic programs"
    - "inductive learning of answer set programs from noisy examples"

Connected through hyperedge HE-3 (induced by source paper "bittensor an intermodel intelligence measure"):
  with members:
    - "learning home crowdsourced training of large neural networks using decentralized mixture of experts"
    - "smooth markets a basic mechanism for organizing gradient based learners"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "cross lingual language model pretraining"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "nisp pruning networks using neuron importance score propagation"

Connected through hyperedge HE-4 (induced by source paper "why we need an ai resilient society"):
  with members:
    - "learning to detect fake face images in the wild"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "turing s red flag"

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)