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: "generalization properties and implicit regularization for multiple passes sgm"
  Abstract: We study the generalization properties of stochastic gradient methods for learning with convex loss functions and linearly parameterized functions. We show that, in the absence of penalizations or constraints, the stability and approximation properties of the algorithm can be controlled by tuning either the step-size or the number of passes over the data. In this view, these parameters can be seen to control a form of implicit regularization. Numerical results complement the theoretical findings.

Connected through hyperedge HE-1 (induced by source paper "increasing generality in machine learning through procedural content generation"):
  with members:
    - "fully differentiable procedural content generation through generative playing networks"
    - "pcgrl procedural content generation via reinforcement learning"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "leveraging procedural generation to benchmark reinforcement learning"
    - "procedural content generation through quality diversity"
    - "arena a general evaluation platform and building toolkit for multi agent intelligence"
    - "continual lifelong learning with neural networks a review"
    - "deceptionnet network driven domain randomization"

Connected through hyperedge HE-2 (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"
    - "understanding deep learning requires rethinking generalization"
    - "generalization of erm in stochastic convex optimization the dimension strikes back"
    - "in search of the real inductive bias on the role of implicit regularization in deep learning"

Connected through hyperedge HE-3 (induced by source paper "procedural content generation from automatically generating game levels to increasing generality in machine learning"):
  with members:
    - "solving rubik s cube with a robot hand"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "continual lifelong learning with neural networks a review"
    - "deceptionnet network driven domain randomization"
    - "meta sim learning to generate synthetic datasets"
    - "the animal ai environment training and testing animal like artificial cognition"
    - "bootstrapping conditional gans for video game level generation"
    - "intentional computational level design"

Connected through hyperedge HE-4 (induced by source paper "implicit regularization in deep matrix factorization"):
  with members:
    - "implicit regularization of discrete gradient dynamics in linear neural networks"
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "width provably matters in optimization for deep linear 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"
    - "algorithmic regularization in learning deep homogeneous models layers are automatically balanced"
    - "a convergence analysis of gradient descent for deep linear neural networks"

Connected through hyperedge HE-5 (induced by source paper "large scale kernel methods and applications to lifelong robot learning"):
  with members:
    - "on the equivalence between quadrature rules and random features"
    - "train faster generalize better stability of stochastic gradient descent"
    - "multimodal deep learning for robust rgb d object recognition"
    - "incremental gradient subgradient and proximal methods for convex optimization a survey"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "efficient algorithms and error analysis for the modified nystrom method"
    - "scalable kernel methods via doubly stochastic gradients"

Connected through hyperedge HE-6 (induced by source paper "bad global minima exist and sgd can reach them"):
  with members:
    - "cinic 10 is not imagenet or cifar 10"
    - "exploring generalization in deep learning"
    - "implicit regularization in deep learning"
    - "geometry of optimization and implicit regularization in deep learning"
    - "the power of interpolation understanding the effectiveness of sgd in modern over parametrized learning"
    - "sgd learns over parameterized networks that provably generalize on linearly separable data"
    - "theory of deep learning iii explaining the non overfitting puzzle"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-7 (induced by source paper "adaptive ensemble of classifiers with regularization for imbalanced data classification"):
  with members:
    - "focal loss for dense object detection"
    - "xgboost a scalable tree boosting system"
    - "noiseout a simple way to prune neural networks"
    - "understanding deep learning requires rethinking generalization"
    - "deep learning in neural networks"
    - "meta des"
    - "cost sensitive learning of deep feature representations from imbalanced data"
    - "the numpy array a structure for efficient numerical computation"

Connected through hyperedge HE-8 (induced by source paper "implicit regularization of accelerated methods in hilbert spaces"):
  with members:
    - "implicit bias of gradient descent on linear convolutional networks"
    - "optimal learning for multi pass stochastic gradient methods"
    - "train faster generalize better stability of stochastic gradient descent"

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)