human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "margin maximization as lossless maximal compression"):
  Abstract: The ultimate goal of a supervised learning algorithm is to produce models constructed on the training data that can generalize well to new examples. In classification, functional margin maximization -- correctly classifying as many training examples as possible with maximal confidence --has been known to construct models with good generalization guarantees. This work gives an information-theoretic interpretation of a margin maximizing model on a noiseless training dataset as one that achieves lossless maximal compression of said dataset -- i.e. extracts from the features all the useful information for predicting the label and no more. The connection offers new insights on generalization in supervised machine learning, showing margin maximization as a special case (that of classification) of a more general principle and explains the success and potential limitations of popular learning algorithms like gradient boosting. We support our observations with theoretical arguments and empirical evidence and identify interesting directions for future work.

Includes members:
  Member "training behavior of deep neural network in frequency domain":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "is the meta learning idea able to improve the generalization of deep neural networks on the standard supervised learning")
  Member "chaining mutual information and tightening generalization bounds":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "reasoning about generalization via conditional mutual information")
  Member "information theoretic analysis of generalization capability of learning algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views")
  Member "exploring generalization in deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the implicit and explicit regularization effects of dropout")
  Member "opening the black box of deep neural networks via information":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views")
  Member "a brief introduction to machine learning for engineers":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "from learning to meta learning reduced training overhead and complexity for communication systems")
  Member "deep variational information bottleneck":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")

Question: Please tell me which class the source paper 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)