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 "memorization in overparameterized autoencoders"):
  Abstract: The ability of deep neural networks to generalize well in the overparameterized regime has become a subject of significant research interest. We show that overparameterized autoencoders exhibit memorization, a form of inductive bias that constrains the functions learned through the optimization process to concentrate around the training examples, although the network could in principle represent a much larger function class. In particular, we prove that single-layer fully-connected autoencoders project data onto the (nonlinear) span of the training examples. In addition, we show that deep fully-connected autoencoders learn a map that is locally contractive at the training examples, and hence iterating the autoencoder results in convergence to the training examples. Finally, we prove that depth is necessary and provide empirical evidence that it is also sufficient for memorization in convolutional autoencoders. Understanding this inductive bias may shed light on the generalization properties of overparametrized deep neural networks that are currently unexplained by classical statistical theory.

Includes members:
  Member "how do infinite width bounded norm networks look in function space":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "balancedness and alignment are unlikely in linear neural networks")
  Member "greedy layerwise learning can scale to imagenet":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "supervised deep similarity matching")
  Member "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "self orthogonality module a network architecture plug in for learning orthogonal filters")
  Member "learning overparameterized neural networks via stochastic gradient descent on structured data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "deja vu an empirical evaluation of the memorization properties of convnets":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "p3sgd patient privacy preserving sgd for regularizing deep cnns in pathological image classification")
  Member "deep image prior":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "semantic pyramid for image generation")
  Member "sgd learns over parameterized networks that provably generalize on linearly separable data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the global convergence of training deep linear resnets")
  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.CV(Computer Vision and Pattern Recognition)