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: "feature based diversity optimization for problem instance classification"
  Abstract: Understanding the behaviour of heuristic search methods is a challenge. This even holds for simple local search methods such as 2-OPT for the Traveling Salesperson problem. In this paper, we present a general framework that is able to construct a diverse set of instances that are hard or easy for a given search heuristic. Such a diverse set is obtained by using an evolutionary algorithm for constructing hard or easy instances that are diverse with respect to different features of the underlying problem. Examining the constructed instance sets, we show that many combinations of two or three features give a good classification of the TSP instances in terms of whether they are hard to be solved by 2-OPT.

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"
    - "the evolutionary process of image transition in conjunction with box and strip mutation"
    - "artistic style transfer for videos"

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"
    - "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"
    - "learning to generate chairs with convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "discrepancy based evolutionary diversity optimization"):
  with members:
    - "evolutionary image composition using feature covariance matrices"
    - "optimal parameter choices through self adjustment applying the 1 5 th rule in discrete settings"
    - "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"
    - "evolutionary image composition using feature covariance 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)