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: "locality promoting representation learning"
  Abstract: This work investigates fundamental questions related to locating and defining features in convolutional neural networks (CNN). The theoretical investigations guided by the locality principle show that the relevance of locations within a representation decreases with distance from the center. This is aligned with empirical findings across multiple architectures such as VGG, ResNet, Inception, DenseNet and MobileNet. To leverage our insights, we introduce Locality-promoting Regularization (LOCO-REG). It yields accuracy gains across multiple architectures and datasets.

Connected through hyperedge HE-1 (induced by source paper "correlated initialization for correlated data"):
  with members:
    - "fixup initialization residual learning without normalization"
    - "adversarial initialization when your network performs the way i want"
    - "acnet strengthening the kernel skeletons for powerful cnn via asymmetric convolution blocks"
    - "rethinking imagenet pre training"
    - "towards understanding regularization in batch normalization"
    - "mean field residual networks on the edge of chaos"
    - "the shattered gradients problem if resnets are the answer then what is the question"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

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)