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: "cortical prediction markets"
  Abstract: We investigate cortical learning from the perspective of mechanism design. First, we show that discretizing standard models of neurons and synaptic plasticity leads to rational agents maximizing simple scoring rules. Second, our main result is that the scoring rules are proper, implying that neurons faithfully encode expected utilities in their synaptic weights and encode high-scoring outcomes in their spikes. Third, with this foundation in hand, we propose a biologically plausible mechanism whereby neurons backpropagate incentives which allows them to optimize their usefulness to the rest of cortex. Finally, experiments show that networks that backpropagate incentives can learn simple tasks.

Connected through hyperedge HE-1 (induced by source paper "smooth markets a basic mechanism for organizing gradient based learners"):
  with members:
    - "logan latent optimisation for generative adversarial networks"
    - "finite regret and cycles with fixed step size via alternating gradient descent ascent"
    - "a closer look at the optimization landscapes of generative adversarial networks"
    - "differentiable game mechanics"
    - "the mechanics of n player differentiable games"
    - "global convergence to the equilibrium of gans using variational inequalities"
    - "the numerics of gans"
    - "multiplicative weights update with constant step size in congestion games convergence limit cycles and chaos"

Connected through hyperedge HE-2 (induced by source paper "deep online convex optimization with gated games"):
  with members:
    - "efficient second order online learning by sketching"
    - "toward deeper understanding of neural networks the power of initialization and a dual view on expressivity"
    - "benefits of depth in neural networks"
    - "ensemble robustness of deep learning algorithms"
    - "strongly typed recurrent neural networks"
    - "conditional computation in neural networks for faster models"
    - "the power of depth for feedforward neural networks"
    - "deep learning in neural networks"

Connected through hyperedge HE-3 (induced by source paper "semantics representations and grammars for deep learning"):
  with members:
    - "deep online convex optimization by putting forecaster to sleep"
    - "blocks and fuel frameworks for deep learning"
    - "deep learning in neural networks"
    - "fast convergence of regularized learning in games"
    - "continuous control with deep reinforcement learning"
    - "gradient estimation using stochastic computation graphs"
    - "compatible value gradients for reinforcement learning of continuous deep policies"
    - "train faster generalize better stability of stochastic gradient descent"

Connected through hyperedge HE-4 (induced by source paper "deep online convex optimization by putting forecaster to sleep"):
  with members:
    - "deep learning in neural networks"
    - "train faster generalize better stability of stochastic gradient descent"
    - "kickback cuts backprop s red tape biologically plausible credit assignment in neural networks"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "breaking the curse of dimensionality with convex neural networks"
    - "randomized co training from cortical neurons to machine learning and back again"

Connected through hyperedge HE-5 (induced by source paper "kickback cuts backprop s red tape biologically plausible credit assignment in neural networks"):
  with members:
    - "randomized co training from cortical neurons to machine learning and back again"
    - "reinforcement learning via aixi approximation"

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)