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 "student specialization in deep relu networks with finite width and input dimension"):
  Abstract: In this paper, we adopt a student-teacher setting to analyze the phenomenon of student specialization, when both teacher and student are deep ReLU networks and student is over-realized (i.e., more student nodes than teacher at each layer). In such a setting, the student network learns from the output of a fixed teacher network of the same depth with Stochastic Gradient Descent (SGD). Our contributions are two-fold. First, we prove that when the gradient is small at every training sample, student nodes \emph{specialize} to teacher nodes in the lowest layer under mild conditions. Second, analysis of noisy recovery and training dynamics in 2-layer network shows that strong teacher nodes (with large fan-out weights) are specialized by student first and weak ones are left unlearned until late stage of training. As a result, it could take a long time to converge into these small-gradient critical points. Our analysis shows that over-realization is an important factor to enable specialization at the critical points, and helps more student nodes specialize to teacher nodes with fewer iterations. Different from Neural Tangent Kernel and statistical mechanics approach, our approach operates on finite width, mild degrees of over-realization and finite input dimension. Experiments justify our finding. The code is released in https://github.com/facebookresearch/luckmatters.

Includes members:
  Member "luck matters understanding training dynamics of deep relu networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "over parameterization as a catalyst for better generalization of deep relu network")
  Member "measuring the intrinsic dimension of objective landscapes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "improving generalization by controlling label noise information in neural network weights")
  Member "small nonlinearities in activation functions create bad local minima in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "understanding global loss landscape of one hidden layer relu neural networks")
  Member "the committee machine computational to statistical gaps in learning a two layers neural network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "over parameterization as a catalyst for better generalization of deep relu network")
  Member "gradient descent finds global minima of deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "learning overparameterized neural networks via stochastic gradient descent on structured data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "mass an accelerated stochastic method for over parametrized learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron")
  Member "a convergence theory for deep learning via over parameterization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "backward feature correction how deep learning performs deep 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)