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: "understanding deep learning generalization by maximum entropy"
  Abstract: Deep learning achieves remarkable generalization capability with overwhelming number of model parameters. Theoretical understanding of deep learning generalization receives recent attention yet remains not fully explored. This paper attempts to provide an alternative understanding from the perspective of maximum entropy. We first derive two feature conditions that softmax regression strictly apply maximum entropy principle. DNN is then regarded as approximating the feature conditions with multilayer feature learning, and proved to be a recursive solution towards maximum entropy principle. The connection between DNN and maximum entropy well explains why typical designs such as shortcut and regularization improves model generalization, and provides instructions for future model development.

Connected through hyperedge HE-1 (induced by source paper "understanding autoencoders with information theoretic concepts"):
  with members:
    - "an introduction to deep learning for the physical layer"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "interpreting cnn knowledge via an explanatory graph"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "opening the black box of deep neural networks via information"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "estimating mixture entropy with pairwise distances"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-2 (induced by source paper "training behavior of deep neural network in frequency domain"):
  with members:
    - "frequency principle fourier analysis sheds light on deep neural networks"
    - "meta learners learning dynamics are unlike learners"
    - "frequency principle in deep learning with general loss functions and its potential application"
    - "understanding training and generalization in deep learning by fourier analysis"
    - "nonlinear collaborative scheme for deep neural networks"
    - "rethinking generalization requires revisiting old ideas statistical mechanics approaches and complex learning behavior"
    - "towards understanding generalization of deep learning perspective of loss landscapes"
    - "opening the black box of deep neural networks via information"

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)