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 "exact expressions for double descent and implicit regularization via surrogate random design"):
  Abstract: Double descent refers to the phase transition that is exhibited by the generalization error of unregularized learning models when varying the ratio between the number of parameters and the number of training samples. The recent success of highly over-parameterized machine learning models such as deep neural networks has motivated a theoretical analysis of the double descent phenomenon in classical models such as linear regression which can also generalize well in the over-parameterized regime. We build on recent advances in Randomized Numerical Linear Algebra (RandNLA) to provide the first exact non-asymptotic expressions for double descent of the minimum norm linear estimator. Our approach involves constructing what we call a surrogate random design to replace the standard i.i.d. design of the training sample. This surrogate design admits exact expressions for the mean squared error of the estimator while preserving the key properties of the standard design. We also establish an exact implicit regularization result for over-parameterized training samples. In particular, we show that, for the surrogate design, the implicit bias of the unregularized minimum norm estimator precisely corresponds to solving a ridge-regularized least squares problem on the population distribution.

Includes members:
  Member "understanding overfitting peaks in generalization error analytical risk curves for l 2 and l 1 penalized interpolation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "harmless interpolation of noisy data in regression")
  Member "traditional and heavy tailed self regularization in neural network models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "resilient cyberphysical systems and their application drivers a technology roadmap")
  Member "two models of double descent for weak features":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a deep conditioning treatment of neural networks")
  Member "harmless interpolation of noisy data in regression":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rethinking parameter counting in deep models effective dimensionality revisited")
  Member "distributed estimation of the inverse hessian by determinantal averaging":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "bayesian experimental design using regularized determinantal point processes")
  Member "implicit regularization in deep matrix factorization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the implicit and explicit regularization effects of dropout")
  Member "bayesian experimental design using regularized determinantal point processes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "improved guarantees and a multiple descent curve for the column subset selection problem and the nystr om method")
  Member "minimax experimental design bridging the gap between statistical and worst case approaches to least squares regression":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "improved guarantees and a multiple descent curve for the column subset selection problem and the nystr om method")

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)