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: "evolutionary image composition using feature covariance matrices"
  Abstract: Evolutionary algorithms have recently been used to create a wide range of artistic work. In this paper, we propose a new approach for the composition of new images from existing ones, that retain some salient features of the original images. We introduce evolutionary algorithms that create new images based on a fitness function that incorporates feature covariance matrices associated with different parts of the images. This approach is very flexible in that it can work with a wide range of features and enables targeting specific regions in the images. For the creation of the new images, we propose a population-based evolutionary algorithm with mutation and crossover operators based on random walks. Our experimental results reveal a spectrum of aesthetically pleasing images that can be obtained with the aid of our evolutionary process.

Connected through hyperedge HE-1 (induced by source paper "evolutionary image transition and painting using random walks"):
  with members:
    - "evolutionary diversity optimization using multi objective indicators"
    - "ponyge2 grammatical evolution in python"
    - "quasi random agents for image transition and animation"
    - "visual art inspired by the collective feeding behavior of sand bubbler crabs"
    - "perceptual losses for real time style transfer and super resolution"
    - "the evolutionary process of image transition in conjunction with box and strip mutation"
    - "artistic style transfer for videos"
    - "controlling perceptual factors in neural style transfer"

Connected through hyperedge HE-2 (induced by source paper "evolution of images with diversity and constraints using a generator network"):
  with members:
    - "the evolutionary process of image transition in conjunction with box and strip mutation"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"
    - "generating images with perceptual similarity metrics based on deep networks"
    - "controlling perceptual factors in neural style transfer"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "texture synthesis using convolutional neural networks"
    - "feature based diversity optimization for problem instance classification"
    - "learning to generate chairs with convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "discrepancy based evolutionary diversity optimization"):
  with members:
    - "optimal parameter choices through self adjustment applying the 1 5 th rule in discrete settings"
    - "feature based diversity optimization for problem instance classification"
    - "constructing low star discrepancy point sets with genetic algorithms"

Connected through hyperedge HE-4 (induced by source paper "evolutionary diversity optimization using multi objective indicators"):
  with members:
    - "discrepancy based evolutionary diversity optimization"
    - "feature based diversity optimization for problem instance 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)