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: "randomized co training from cortical neurons to machine learning and back again"
  Abstract: Despite its size and complexity, the human cortex exhibits striking anatomical regularities, suggesting there may simple meta-algorithms underlying cortical learning and computation. We expect such meta-algorithms to be of interest since they need to operate quickly, scalably and effectively with little-to-no specialized assumptions. #R##N#This note focuses on a specific question: How can neurons use vast quantities of unlabeled data to speed up learning from the comparatively rare labels provided by reward systems? As a partial answer, we propose randomized co-training as a biologically plausible meta-algorithm satisfying the above requirements. As evidence, we describe a biologically-inspired algorithm, Correlated Nystrom Views (XNV) that achieves state-of-the-art performance in semi-supervised learning, and sketch work in progress on a neuronal implementation.

Connected through hyperedge HE-1 (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-2 (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-3 (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"
    - "cortical prediction markets"

Connected through hyperedge HE-4 (induced by source paper "kickback cuts backprop s red tape biologically plausible credit assignment in neural networks"):
  with members:
    - "cortical prediction markets"
    - "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.LG(Machine Learning)