human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "in search of the real inductive bias on the role of implicit regularization in deep learning"
  Abstract: We present experiments demonstrating that some other form of capacity control, different from network size, plays a central role in learning multilayer feed-forward networks. We argue, partially through analogy to matrix factorization, that this is an inductive bias that can help shed light on deep learning.

Connected through hyperedge HE-1 (induced by source paper "implicit geometric regularization for learning shapes"):
  with members:
    - "controlling neural level sets"
    - "sdfdiff differentiable rendering of signed distance fields for 3d shape optimization"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "cvxnets learnable convex decomposition"
    - "learning elementary structures for 3d shape generation and matching"
    - "bsp net generating compact meshes via binary space partitioning"
    - "voronoinet general functional approximators with local support"
    - "deep level sets implicit surface representations for 3d shape inference"

Connected through hyperedge HE-2 (induced by source paper "unique properties of wide minima in deep networks"):
  with members:
    - "a tail index analysis of stochastic gradient noise in deep neural networks"
    - "implicit regularization of normalization methods"
    - "gradient descent maximizes the margin of homogeneous neural networks"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "gradient descent aligns the layers of deep linear networks"
    - "step size matters in deep learning"
    - "algorithmic regularization in learning deep homogeneous models layers are automatically balanced"

Connected through hyperedge HE-3 (induced by source paper "hyperplane arrangements of trained convnets are biased"):
  with members:
    - "geometry of deep convolutional networks"
    - "an empirical study of example forgetting during deep neural network learning"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "three factors influencing minima in sgd"
    - "exploring generalization in deep learning"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "bounding and counting linear regions of deep neural networks"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-4 (induced by source paper "double trouble in double descent bias and variance s in the lazy regime"):
  with members:
    - "a jamming transition from under to over parametrization affects generalization in deep learning"
    - "two models of double descent for weak features"
    - "disentangling feature and lazy learning in deep neural networks an empirical study"
    - "kernel and deep regimes in overparametrized models"
    - "harnessing the power of infinitely wide deep nets on small data tasks"
    - "deep double descent where bigger models and more data hurt"
    - "on exact computation with an infinitely wide neural net"
    - "a modern take on the bias variance tradeoff in neural networks"

Connected through hyperedge HE-5 (induced by source paper "balancedness and alignment are unlikely in linear neural networks"):
  with members:
    - "how do infinite width bounded norm networks look in function space"
    - "memorization in overparameterized autoencoders"
    - "global convergence of adaptive gradient methods for an over parameterized neural network"
    - "implicit regularization in deep matrix factorization"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "gradient descent aligns the layers of deep linear networks"

Connected through hyperedge HE-6 (induced by source paper "neural networks are convex regularizers exact polynomial time convex optimization formulations for two layer networks"):
  with members:
    - "how do infinite width bounded norm networks look in function space"
    - "on exact computation with an infinitely wide neural net"
    - "complexity of training relu neural networks"
    - "principled deep neural network training through linear programming"
    - "a convergence theory for deep learning via over parameterization"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "reliably learning the relu in polynomial time"
    - "understanding deep neural networks with rectified linear units"

Connected through hyperedge HE-7 (induced by source paper "convex duality of deep neural networks"):
  with members:
    - "width provably matters in optimization for deep linear neural networks"
    - "implicit regularization in deep matrix factorization"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "exponential convergence time of gradient descent for one dimensional deep linear neural networks"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "gradient descent aligns the layers of deep linear networks"
    - "a convergence analysis of gradient descent for deep linear neural networks"
    - "deep linear neural networks with arbitrary loss all local minima are global"

Connected through hyperedge HE-8 (induced by source paper "can implicit bias explain generalization stochastic convex optimization as a case study"):
  with members:
    - "uniform convergence may be unable to explain generalization in deep learning"
    - "implicit regularization in deep matrix factorization"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "implicit regularization in deep learning"
    - "geometry of optimization and implicit regularization in deep learning"
    - "generalization properties and implicit regularization for multiple passes sgm"
    - "understanding deep learning requires rethinking generalization"
    - "generalization of erm in stochastic convex optimization the dimension strikes back"

Question: Please tell me which class the center node 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)