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 "universal approximation with quadratic deep networks"):
  Abstract: Recently, deep learning has achieved huge successes in many important applications. In our previous studies, we proposed quadratic/second-order neurons and deep quadratic neural networks. In a quadratic neuron, the inner product of a vector of data and the corresponding weights in a conventional neuron is replaced with a quadratic function. The resultant quadratic neuron enjoys an enhanced expressive capability over the conventional neuron. However, how quadratic neurons improve the expressing capability of a deep quadratic network has not been studied up to now, preferably in relation to that of a conventional neural network. Regarding this, we ask four basic questions in this paper: (1) for the one-hidden-layer network structure, is there any function that a quadratic network can approximate much more efficiently than a conventional network? (2) for the same multi-layer network structure, is there any function that can be expressed by a quadratic network but cannot be expressed with conventional neurons in the same structure? (3) Does a quadratic network give a new insight into universal approximation? (4) To approximate the same class of functions with the same error bound, is a quantized quadratic network able to enjoy a lower number of weights than a quantized conventional network? Our main contributions are the four interconnected theorems shedding light upon these four questions and demonstrating the merits of a quadratic network in terms of expressive efficiency, unique capability, compact architecture and computational capacity respectively.

Includes members:
  Member "quadratic autoencoder q ae for low dose ct denoising":
    also connected through other hyperedges:
      (none)
  Member "on the universal approximability and complexity bounds of quantized relu neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dlbc a deep learning based consensus in blockchains for deep learning services")
  Member "efficient sparse winograd convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dwm a decomposable winograd method for convolution acceleration")
  Member "resnet with one neuron hidden layers is a universal approximator":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "lossless compression of deep neural networks")
  Member "fuzzy logic interpretation of artificial neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "quadratic autoencoder q ae for low dose ct denoising")
  Member "dense associative memory is robust to adversarial inputs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "quadratic autoencoder q ae for low dose ct denoising")
  Member "on the power of over parametrization in neural networks with quadratic activation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "knowledge distillation via adaptive instance normalization")
  Member "a new type of neurons for machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "quadratic autoencoder q ae for low dose ct denoising")

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)