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: "laprop a better way to combine momentum with adaptive gradient"
  Abstract: Identifying a divergence problem in Adam, we propose a new optimizer, LaProp, which belongs to the family of adaptive gradient descent methods. This method allows for greater flexibility in choosing its hyperparameters, mitigates the effort of fine tuning, and permits straightforward interpolation between the signed gradient methods and the adaptive gradient methods. We bound the regret of LaProp on a convex problem and show that our bound differs from the previous methods by a key factor, which demonstrates its advantage. We experimentally show that LaProp outperforms the previous methods on a toy task with noisy gradients, optimization of extremely deep fully-connected networks, neural art style transfer, natural language processing using transformers, and reinforcement learning with deep-Q networks. The performance improvement of LaProp is shown to be consistent, sometimes dramatic and qualitative.

Connected through hyperedge HE-1 (induced by source paper "learning not to learn in the presence of noisy labels"):
  with members:
    - "deep gamblers learning to abstain with portfolio theory"
    - "sgd on neural networks learns functions of increasing complexity"
    - "simple and effective regularization methods for training on noisily labeled data with generalization guarantee"
    - "how does disagreement help generalization against label corruption"
    - "selectivenet a deep neural network with an integrated reject option"
    - "l_dmi an information theoretic noise robust loss function"
    - "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks"
    - "gradient descent finds global minima of deep neural networks"

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)