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 "rethinking generalization requires revisiting old ideas statistical mechanics approaches and complex learning behavior"):
  Abstract: We describe an approach to understand the peculiar and counterintuitive generalization properties of deep neural networks. The approach involves going beyond worst-case theoretical capacity control frameworks that have been popular in machine learning in recent years to revisit old ideas in the statistical mechanics of neural networks. Within this approach, we present a prototypical Very Simple Deep Learning (VSDL) model, whose behavior is controlled by two control parameters, one describing an effective amount of data, or load, on the network (that decreases when noise is added to the input), and one with an effective temperature interpretation (that increases when algorithms are early stopped). Using this model, we describe how a very simple application of ideas from the statistical mechanics theory of generalization provides a strong qualitative description of recently-observed empirical results regarding the inability of deep neural networks not to overfit training data, discontinuous learning and sharp transitions in the generalization properties of learning algorithms, etc.

Includes members:
  Member "traditional and heavy tailed self regularization in neural network models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "resilient cyberphysical systems and their application drivers a technology roadmap")
  Member "heavy tailed universality predicts trends in test accuracies for very large pre trained deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "resilient cyberphysical systems and their application drivers a technology roadmap")
  Member "deep learning is robust to massive label noise":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "derivative manipulation for general example weighting")
  Member "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "self orthogonality module a network architecture plug in for learning orthogonal filters")
  Member "theory iiib generalization in deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "think global act local relating dnn generalisation and node level snr")
  Member "a surprising linear relationship predicts test performance in deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an empirical study of large batch stochastic gradient descent with structured covariance noise")
  Member "on the computational inefficiency of large batch sizes for stochastic gradient descent":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "distributed stochastic algorithms for high rate streaming principal component analysis")
  Member "measuring the effects of data parallelism on neural network training":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)