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 algorithmic intelligence a mathematical top down approach"
  Abstract: Sequential decision theory formally solves the problem of rational agents in uncertain worlds if the true environmental prior probability distribution is known. Solomonoff's theory of universal induction formally solves the problem of sequence prediction for unknown prior distribution. We combine both ideas and get a parameter-free theory of universal Artificial Intelligence. We give strong arguments that the resulting AIXI model is the most intelligent unbiased agent possible. We outline how the AIXI model can formally solve a number of problem classes, including sequence prediction, strategic games, function minimization, reinforcement and supervised learning. The major drawback of the AIXI model is that it is uncomputable. To overcome this problem, we construct a modified algorithm AIXItl that is still effectively more intelligent than any other time t and length l bounded agent. The computation time of AIXItl is of the order t x 2^l. The discussion includes formal definitions of intelligence order relations, the horizon problem and relations of the AIXI theory to other AI approaches.

Connected through hyperedge HE-1 (induced by source paper "corrigibility with utility preservation"):
  with members:
    - "agi safety literature review"
    - "ai safety gridworlds"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "incorrigibility in the cirl framework"
    - "self modification of policy and utility function in rational agents"

Connected through hyperedge HE-2 (induced by source paper "the foundations of deep learning with a path towards general intelligence"):
  with members:
    - "progressive neural networks"
    - "a simple neural network module for relational reasoning"
    - "dynamic routing between capsules"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "neural task programming learning to generalize across hierarchical tasks"
    - "geometric deep learning going beyond euclidean data"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "imagination augmented agents for deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "omega an architecture for ai unification"):
  with members:
    - "evolutionary architecture search for deep multitask networks"
    - "imagination augmented agents for deep reinforcement learning"
    - "video summarization with long short term memory"
    - "deep convolutional inverse graphics network"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "church a language for generative models"
    - "normalized information distance"

Connected through hyperedge HE-4 (induced by source paper "computable variants of aixi which are more powerful than aixitl"):
  with members:
    - "a monte carlo aixi approximation"

Connected through hyperedge HE-5 (induced by source paper "ultimate intelligence part iii measures of intelligence perception and intelligent agents"):
  with members:
    - "some theorems on the algorithmic approach to probability theory and information theory"
    - "optimal ordered problem solver"

Connected through hyperedge HE-6 (induced by source paper "limits to verification and validation of agentic behavior"):
  with members:
    - "research priorities for robust and beneficial artificial intelligence"
    - "friendly artificial intelligence the physics challenge"

Connected through hyperedge HE-7 (induced by source paper "ultimate intelligence part i physical completeness and objectivity of induction"):
  with members:
    - "an approximation of the universal intelligence measure"
    - "non equivalence of universal priors"
    - "models of quantum computation and quantum programming languages"
    - "some theorems on the algorithmic approach to probability theory and information theory"
    - "convergence and loss bounds for bayesian sequence prediction"
    - "optimal ordered problem solver"

Connected through hyperedge HE-8 (induced by source paper "ultimate intelligence part ii physical measure and complexity of intelligence"):
  with members:
    - "some theorems on the algorithmic approach to probability theory and information theory"
    - "optimal ordered problem solver"

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)