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: "evolving unsupervised deep neural networks for learning meaningful representations"
  Abstract: Deep learning (DL) aims at learning the  meaningful representations . A meaningful representation gives rise to significant performance improvement of associated machine learning (ML) tasks by replacing the raw data as the input. However, optimal architecture design and model parameter estimation in DL algorithms are widely considered to be intractable. Evolutionary algorithms are much preferable for complex and nonconvex problems due to its inherent characteristics of gradient-free and insensitivity to the local optimal. In this paper, we propose a computationally economical algorithm for evolving  unsupervised deep neural networks  to efficiently learn  meaningful representations , which is very suitable in the current big data era where sufficient labeled data for training is often expensive to acquire. In the proposed algorithm, finding an appropriate architecture and the initialized parameter values for an ML task at hand is modeled by one computational efficient gene encoding approach, which is employed to effectively model the task with a large number of parameters. In addition, a local search strategy is incorporated to facilitate the exploitation search for further improving the performance. Furthermore, a small proportion labeled data is utilized during evolution search to guarantee the learned representations to be meaningful. The performance of the proposed algorithm has been thoroughly investigated over classification tasks. Specifically, error classification rate on MNIST with 1.15% is reached by the proposed algorithm consistently, which is considered a very promising result against state-of-the-art unsupervised DL algorithms.

Connected through hyperedge HE-1 (induced by source paper "multi objective genetic programming for manifold learning balancing quality and dimensionality"):
  with members:
    - "can genetic programming do manifold learning too"
    - "unsupervised learning of disentangled and interpretable representations from sequential data"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-2 (induced by source paper "genetic programming for evolving a front of interpretable models for data visualisation"):
  with members:
    - "can genetic programming do manifold learning too"
    - "dimreader axis lines that explain non linear projections"

Connected through hyperedge HE-3 (induced by source paper "on the bounds of function approximations"):
  with members:
    - "size free generalization bounds for convolutional neural networks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "auto keras an efficient neural architecture search system"
    - "neural architecture optimization"
    - "darts differentiable architecture search"
    - "efficient neural architecture search via parameter sharing"
    - "transfer learning with neural automl"
    - "large scale evolution of image classifiers"

Connected through hyperedge HE-4 (induced by source paper "a particle swarm optimization based flexible convolutional autoencoder for image classification"):
  with members:
    - "large scale evolution of image classifiers"
    - "convolution by evolution differentiable pattern producing networks"
    - "designing neural network architectures using reinforcement learning"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "deep clustered convolutional kernels"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "autoencoding with xcsf"):
  with members:
    - "weight agnostic neural networks"
    - "autoencoding with a learning classifier system initial results"
    - "evolutionary stochastic gradient descent for optimization of deep neural networks"
    - "population based training of neural networks"
    - "convolution by evolution differentiable pattern producing networks"
    - "a cognitive architecture based on a learning classifier system with spiking classifiers"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-6 (induced by source paper "can genetic programming do manifold learning too"):
  with members:
    - "a particle swarm optimization based flexible convolutional autoencoder for image classification"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-7 (induced by source paper "automatically designing cnn architectures using genetic algorithm for image classification"):
  with members:
    - "igd indicator based evolutionary algorithm for many objective optimization problems"
    - "improved regularity model based eda for many objective optimization"
    - "transfer learning based dynamic multiobjective optimization algorithms"
    - "large scale evolution of image classifiers"
    - "skip connections eliminate singularities"
    - "efficient architecture search by network transformation"
    - "hierarchical representations for efficient architecture search"
    - "a genetic programming approach to designing convolutional neural network architectures"

Connected through hyperedge HE-8 (induced by source paper "evolving deep convolutional neural networks by variable length particle swarm optimization for image classification"):
  with members:
    - "a particle swarm optimization based flexible convolutional autoencoder for image classification"
    - "large scale evolution of image classifiers"
    - "evolving deep convolutional neural networks for image classification"
    - "a genetic programming approach to designing convolutional neural network architectures"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "convolutional neural networks for sentence classification"

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)