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: "divide and conquer local average regression"
  Abstract: The divide and conquer strategy, which breaks a massive data set into a se- ries of manageable data blocks, and then combines the independent results of data blocks to obtain a final decision, has been recognized as a state-of-the-art method to overcome challenges of massive data analysis. In this paper, we merge the divide and conquer strategy with local average regression methods to infer the regressive relationship of input-output pairs from a massive data set. After theoretically analyzing the pros and cons, we find that although the divide and conquer local average regression can reach the optimal learning rate, the restric- tion to the number of data blocks is a bit strong, which makes it only feasible for small number of data blocks. We then propose two variants to lessen (or remove) this restriction. Our results show that these variants can achieve the optimal learning rate with much milder restriction (or without such restriction). Extensive experimental studies are carried out to verify our theoretical assertions.

Connected through hyperedge HE-1 (induced by source paper "theoretical analysis of divide and conquer erm beyond square loss and rkhs"):
  with members:
    - "empirical risk minimization for stochastic convex optimization o 1 n and o 1 n 2 type of risk bounds"
    - "distributed learning with regularized least squares"
    - "refined error bounds for several learning algorithms"
    - "distributed autonomous online learning regrets and intrinsic privacy preserving properties"
    - "optimistic rates for learning with a smooth loss"

Connected through hyperedge HE-2 (induced by source paper "distributed learning with dependent samples"):
  with members:
    - "realization of spatial sparseness by deep relu nets with massive data"
    - "distributed learning with regularized least squares"
    - "optimality guarantees for distributed statistical estimation"

Connected through hyperedge HE-3 (induced by source paper "construction of neural networks for realization of localized deep learning"):
  with members:
    - "efficient representation of low dimensional manifolds using deep networks"
    - "deep vs shallow networks an approximation theory perspective"
    - "benefits of depth in neural networks"
    - "learning functions when is deep better than shallow"
    - "why and when can deep but not shallow networks avoid the curse of dimensionality a review"
    - "the power of depth for feedforward neural networks"
    - "consistency analysis of an empirical minimum error entropy algorithm"
    - "an algorithm for training polynomial networks"

Connected through hyperedge HE-4 (induced by source paper "constructive neural network learning"):
  with members:
    - "sharp analysis of low rank kernel matrix approximations"

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)