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: "optimal ordered problem solver"
  Abstract: We present a novel, general, optimally fast, incremental way of searching for a universal algorithm that solves each task in a sequence of tasks. The Optimal Ordered Problem Solver (OOPS) continually organizes and exploits previously found solutions to earlier tasks, efficiently searching not only the space of domain-specific algorithms, but also the space of search algorithms. Essentially we extend the principles of optimal nonincremental universal search to build an incremental universal learner that is able to improve itself through experience. In illustrative experiments, our self-improver becomes the first general system that learns to solve all n disk Towers of Hanoi tasks (solution size 2^n-1) for n up to 30, profiting from previously solved, simpler tasks involving samples of a simple context free language.

Connected through hyperedge HE-1 (induced by source paper "automl zero evolving machine learning algorithms from scratch"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "nas evaluation is frustratingly hard"
    - "towards modular and programmable architecture search"
    - "weight agnostic neural networks"
    - "exploring randomly wired neural networks for image recognition"
    - "the evolved transformer"
    - "randaugment practical automated data augmentation with a reduced search space"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"

Connected through hyperedge HE-2 (induced by source paper "online meta learning"):
  with members:
    - "learning to learn stochastic gradient descent with biased regularization"
    - "growing a brain fine tuning by increasing model capacity"
    - "introduction to online convex optimization"
    - "provable guarantees for gradient based meta learning"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "truncated back propagation for bilevel optimization"
    - "how to train your maml"
    - "plan online learn offline efficient learning and exploration via model based control"

Connected through hyperedge HE-3 (induced by source paper "write execute assess program synthesis with a repl"):
  with members:
    - "learning to infer program sketches"
    - "learning to infer and execute 3d shape programs"
    - "neural guided deductive search for real time program synthesis from examples"
    - "automatic program synthesis of long programs with a learned garbage collector"
    - "synthesizing programs for images using reinforced adversarial learning"
    - "program synthesis through reinforcement learning guided tree search"
    - "selecting representative examples for program synthesis"
    - "robustfill neural program learning under noisy i o"

Connected through hyperedge HE-4 (induced by source paper "a theory of incremental compression"):
  with members:
    - "universal intelligence a definition of machine intelligence"
    - "the fastest and shortest algorithm for all well defined problems"

Connected through hyperedge HE-5 (induced by source paper "learning to learn without forgetting by maximizing transfer and minimizing interference"):
  with members:
    - "on first order meta learning algorithms"
    - "learning abstract options"
    - "reptile a scalable metalearning algorithm"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "learning robust options"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "lifelong learning with dynamically expandable networks"

Connected through hyperedge HE-6 (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-7 (induced by source paper "learning to optimize neural nets"):
  with members:
    - "learning to learn by gradient descent by gradient descent"
    - "learning to optimize"
    - "deep q networks for accelerating the training of deep neural networks"
    - "end to end training of deep visuomotor policies"
    - "adam a method for stochastic optimization"
    - "neural turing machines"

Connected through hyperedge HE-8 (induced by source paper "arguments for the effectiveness of human problem solving"):
  with members:
    - "the fastest and shortest algorithm for all well defined problems"

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)