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 "a jamming transition from under to over parametrization affects generalization in deep learning"):
  Abstract: We argue that in fully-connected networks a phase transition delimits the over- and under-parametrized regimes where fitting can or cannot be achieved. Under some general conditions, we show that this transition is sharp for the hinge loss. In the whole over-parametrized regime, poor minima of the loss are not encountered during training since the number of constraints to satisfy is too small to hamper minimization. Our findings support a link between this transition and the generalization properties of the network: as we increase the number of parameters of a given model, starting from an under-parametrized network, we observe that the generalization error displays three phases: (i) initial decay, (ii) increase until the transition point --- where it displays a cusp --- and (iii) slow decay toward a constant for the rest of the over-parametrized regime. Thereby we identify the region where the classical phenomenon of over-fitting takes place, and the region where the model keeps improving, in line with previous empirical observations for modern neural networks.

Includes members:
  Member "two models of double descent for weak features":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a deep conditioning treatment of neural networks")
  Member "towards understanding the role of over parametrization in generalization of neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the implicit and explicit regularization effects of dropout")
  Member "the loss landscape of overparameterized neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "toward a theory of optimization for over parameterized systems of non linear equations the lessons of deep learning")
  Member "neural tangent kernel convergence and generalization in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "geometry of optimization and implicit regularization in deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "double trouble in double descent bias and variance s in the lazy regime")
  Member "empirical analysis of the hessian of over parametrized neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the implicit and explicit regularization effects of dropout")
  Member "stuck in a what adventures in weight space":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "classifying the classifier dissecting the weight space of neural networks")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

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)