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 deep convolutional neural networks for image classification"
  Abstract: Evolutionary computation methods have been successfully applied to neural networks since two decades ago, while those methods cannot scale well to the modern deep neural networks due to the complicated architectures and large quantities of connection weights. In this paper, we propose a new method using genetic algorithms for evolving the architectures and connection weight initialization values of a deep convolutional neural network to address image classification problems. In the proposed algorithm, an efficient variable-length gene encoding strategy is designed to represent the different building blocks and the unpredictable optimal depth in convolutional neural networks. In addition, a new representation scheme is developed for effectively initializing connection weights of deep convolutional neural networks, which is expected to avoid networks getting stuck into local minima which is typically a major issue in the backward gradient-based optimization. Furthermore, a novel fitness evaluation method is proposed to speed up the heuristic search with substantially less computational resource. The proposed algorithm is examined and compared with 22 existing algorithms on nine widely used image classification tasks, including the state-of-the-art methods. The experimental results demonstrate the remarkable superiority of the proposed algorithm over the state-of-the-art algorithms in terms of classification error rate and the number of parameters (weights).

Connected through hyperedge HE-1 (induced by source paper "generator evaluator selector net a modular approach for panoptic segmentation"):
  with members:
    - "panoptic segmentation"
    - "learning instance occlusion for panoptic segmentation"
    - "an end to end network for panoptic segmentation"
    - "a review of modularization techniques in artificial neural networks"
    - "upsnet a unified panoptic segmentation network"
    - "selectivenet a deep neural network with an integrated reject option"
    - "class independent sequential full image segmentation using a convolutional net that finds a segment within an attention region given a pointer pixel within this segment"
    - "panoptic feature pyramid networks"

Connected through hyperedge HE-2 (induced by source paper "a hybrid ga pso method for evolving architecture and short connections of deep convolutional neural networks"):
  with members:
    - "a hybrid differential evolution approach to designing deep convolutional neural networks for image classification"
    - "automatically designing cnn architectures using genetic algorithm for image classification"
    - "evolving deep convolutional neural networks by variable length particle swarm optimization for image classification"
    - "skip connections eliminate singularities"
    - "learning transferable architectures for scalable image recognition"
    - "fast and accurate image super resolution by deep cnn with skip connection and network in network"
    - "a genetic programming approach to designing convolutional neural network architectures"
    - "densely connected convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "an empirical exploration of deep recurrent connections and memory cells using neuro evolution"):
  with members:
    - "a specialized evolutionary strategy using mean absolute error random sampling to design recurrent neural networks"
    - "investigating recurrent neural network memory structures using neuro evolution"
    - "low cost recurrent neural network expected performance evaluation"
    - "from nodes to networks evolving recurrent neural networks"
    - "evolving deep neural networks"
    - "gated orthogonal recurrent units on learning to forget"
    - "learning simpler language models with the differential state framework"
    - "a genetic programming approach to designing convolutional neural network architectures"

Connected through hyperedge HE-4 (induced by source paper "investigating recurrent neural network memory structures using neuro evolution"):
  with members:
    - "accelerating the evolution of convolutional neural networks with node level mutations and epigenetic weight initialization"
    - "from nodes to networks evolving recurrent neural networks"
    - "perturbative neural networks"
    - "evolving deep neural networks"
    - "large scale evolution of image classifiers"
    - "learning simpler language models with the differential state framework"
    - "a genetic programming approach to designing convolutional neural network architectures"
    - "large scale evolution of convolutional neural networks using volunteer computing"

Connected through hyperedge HE-5 (induced by source paper "investigating rnn memory using neuro evolution investigating recurrent neural network memory structures using neuro evolution"):
  with members:
    - "from nodes to networks evolving recurrent neural networks"
    - "perturbative neural networks"
    - "learned iterative decoding for lossy image compression systems"
    - "evolving deep neural networks"
    - "large scale evolution of image classifiers"
    - "learning simpler language models with the differential state framework"
    - "a genetic programming approach to designing convolutional neural network architectures"
    - "using neural generative models to release synthetic twitter corpora with reduced stylometric identifiability of users"

Connected through hyperedge HE-6 (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"
    - "evolving unsupervised deep neural networks for learning meaningful representations"
    - "large scale evolution of image classifiers"
    - "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)