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 "enhanced image classification with a fast learning shallow convolutional neural network"):
  Abstract: We present a neural network architecture and training method designed to enable very rapid training and low implementation complexity. Due to its training speed and very few tunable parameters, the method has strong potential for applications requiring frequent retraining or online training. The approach is characterized by (a) convolutional filters based on biologically inspired visual processing filters, (b) randomly-valued classifier-stage input weights, (c) use of least squares regression to train the classifier output weights in a single batch, and (d) linear classifier-stage output units. We demonstrate the efficacy of the method by applying it to image classification. Our results match existing state-of-the-art results on the MNIST (0.37% error) and NORB-small (2.2% error) image classification databases, but with very fast training times compared to standard deep network approaches. The network's performance on the Google Street View House Number (SVHN) (4% error) database is also competitive with state-of-the art methods.

Includes members:
  Member "deep learning in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "π nets deep polynomial neural networks")
  Member "explicit computation of input weights in extreme learning machines":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "exploiting the short term to long term plasticity transition in memristive nanodevice learning architectures")
  Member "fast simple and accurate handwritten digit classification by training shallow neural network classifiers with the extreme learning machine algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "track everything limiting prior knowledge in online multi object recognition")
  Member "constrained extreme learning machines a study on classification cases":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fast simple and accurate handwritten digit classification by training shallow neural network classifiers with the extreme learning machine algorithm")
  Member "convolutional kernel networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "why do deep residual networks generalize better than deep feedforward networks a neural tangent kernel perspective")
  Member "online and adaptive pseudoinverse solutions for elm weights":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adepos a novel approximate computing framework for anomaly detection systems and its implementation in 65nm cmos")
  Member "overfeat integrated recognition localization and detection using convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "2013 special issue learning the pseudoinverse solution to network weights":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adepos a novel approximate computing framework for anomaly detection systems and its implementation in 65nm cmos")

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.NE(Neural and Evolutionary Computing)