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: "the evolutionary process of image transition in conjunction with box and strip mutation"
  Abstract: Evolutionary algorithms have been used in many ways to generate digital art. We study how evolutionary processes are used for evolutionary art and present a new approach to the transition of images. Our main idea is to define evolutionary processes for digital image transition, combining different variants of mutation and evolutionary mechanisms. We introduce box and strip mutation operators which are specifically designed for image transition. Our experimental results show that the process of an evolutionary algorithm in combination with these mutation operators can be used as a valuable way to produce unique generative art.

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"
    - "evolutionary image composition using feature covariance matrices"
    - "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"
    - "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:
    - "evolutionary image composition using feature covariance matrices"
    - "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 "evolutionary image composition using feature covariance matrices"):
  with members:
    - "kernel methods on riemannian manifolds with gaussian rbf kernels"
    - "optimal parameter choices through self adjustment applying the 1 5 th rule in discrete settings"
    - "kernel methods on the riemannian manifold of symmetric positive definite matrices"

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)