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: "an introduction to convolutional neural networks"
  Abstract: The field of machine learning has taken a dramatic twist in recent times, with the rise of the Artificial Neural Network (ANN). These biologically inspired computational models are able to far exceed the performance of previous forms of artificial intelligence in common machine learning tasks. One of the most impressive forms of ANN architecture is that of the Convolutional Neural Network (CNN). CNNs are primarily used to solve difficult image-driven pattern recognition tasks and with their precise yet simple architecture, offers a simplified method of getting started with ANNs. #R##N#This document provides a brief introduction to CNNs, discussing recently published papers and newly formed techniques in developing these brilliantly fantastic image recognition models. This introduction assumes you are familiar with the fundamentals of ANNs and machine learning.

Connected through hyperedge HE-1 (induced by source paper "structural health monitoring of cantilever beam a case study using bayesian neural network and deep learning"):
  with members:
    - "deep learning and its applications to machine health monitoring a survey"
    - "very deep convolutional networks for large scale image recognition"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

Connected through hyperedge HE-2 (induced by source paper "learning representations of graph data a survey"):
  with members:
    - "fast sequence based embedding with diffusion graphs"
    - "adaptive graph convolutional neural networks"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "learning deep generative models of graphs"
    - "relational inductive biases deep learning and graph networks"
    - "motifnet a motif based graph convolutional network for directed graphs"
    - "deriving neural architectures from sequence and graph kernels"
    - "inductive representation learning on large graphs"

Connected through hyperedge HE-3 (induced by source paper "hypernomad hyperparameter optimization of deep neural networks using mesh adaptive direct search"):
  with members:
    - "a novel orthogonal direction mesh adaptive direct search approach for svm hyperparameter tuning"
    - "towards automated deep learning efficient joint neural architecture and hyperparameter search"
    - "regularized evolution for image classifier architecture search"
    - "learning transferable architectures for scalable image recognition"
    - "a genetic programming approach to designing convolutional neural network architectures"
    - "black box optimization in machine learning with trust region based derivative free algorithm"
    - "a survey on deep learning in medical image analysis"
    - "hyperband a novel bandit based approach to hyperparameter optimization"

Connected through hyperedge HE-4 (induced by source paper "training generative networks using random discriminators"):
  with members:
    - "on the convergence and robustness of training gans with regularized optimal transport"
    - "on the behavior of the expectation maximization algorithm for mixture models"
    - "on the implicit assumptions of gans"
    - "the cramer distance as a solution to biased wasserstein gradients"
    - "implicit maximum likelihood estimation"
    - "improved training of wasserstein gans"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "energy based generative adversarial network"

Connected through hyperedge HE-5 (induced by source paper "student engagement detection using emotion analysis eye tracking and head movement with machine learning"):
  with members:
    - "real time convolutional neural networks for emotion and gender classification"

Connected through hyperedge HE-6 (induced by source paper "sparse over complete patch matching"):
  with members:
    - "learning to compare image patches via convolutional neural networks"
    - "adam a method for stochastic optimization"

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.NE(Neural and Evolutionary Computing)