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 "implicit bias of gradient descent on linear convolutional networks"):
  Abstract: We show that gradient descent on full-width linear convolutional networks of depth $L$ converges to a linear predictor related to the $\ell_{2/L}$ bridge penalty in the frequency domain. This is in contrast to linearly fully connected networks, where gradient descent converges to the hard margin linear support vector machine solution, regardless of depth.

Includes members:
  Member "risk and parameter convergence of logistic regression":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a generalization theory of gradient descent for learning over parameterized deep relu networks")
  Member "geometry of optimization and implicit regularization in deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "double trouble in double descent bias and variance s in the lazy regime")
  Member "don t decay the learning rate increase the batch size":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "communication optimization strategies for distributed deep learning a survey")
  Member "sharp minima can generalize for deep nets":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bayesian deep learning and a probabilistic perspective of generalization")
  Member "the loss surface of deep and wide neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "translating diffusion wavelets and regularisation into residual networks")
  Member "algorithmic regularization in over parameterized matrix recovery":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "revisiting landscape analysis in deep neural networks eliminating decreasing paths to infinity")
  Member "learning to learn by gradient descent by gradient descent":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "understanding deep learning requires rethinking generalization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers")

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)