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: "self organizing classifiers and niched fitness"
  Abstract: Learning classifier systems are adaptive learning systems which have been widely applied in a multitude of application domains. However, there are still some generalization problems unsolved. The hurdle is that fitness and niching pressures are difficult to balance. Here, a new algorithm called Self Organizing Classifiers is proposed which faces this problem from a different perspective. Instead of balancing the pressures, both pressures are separated and no balance is necessary. In fact, the proposed algorithm possesses a dynamical population structure that self-organizes itself to better project the input space into a map. The niched fitness concept is defined along with its dynamical population structure, both are indispensable for the understanding of the proposed method. Promising results are shown on two continuous multi-step problems. One of which is yet more challenging than previous problems of this class in the literature.

Connected through hyperedge HE-1 (induced by source paper "one pixel attack for fooling deep neural networks"):
  with members:
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "self organizing classifiers first steps in structured evolutionary machine learning"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "on the suitability of l_p norms for creating and preventing adversarial examples"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "classification regions of deep neural networks"
    - "exploring the landscape of spatial robustness"

Connected through hyperedge HE-2 (induced by source paper "universal rules for fooling deep neural networks based text classification"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "deep text classification can be fooled"
    - "spectrum diverse neuroevolution with unified neural models"
    - "practical black box attacks against machine learning"
    - "universal adversarial perturbations"
    - "practical black box attacks against deep learning systems using adversarial examples"
    - "character level convolutional networks for text classification"

Connected through hyperedge HE-3 (induced by source paper "batch tournament selection for genetic programming"):
  with members:
    - "epsilon lexicase selection for regression"
    - "general subpopulation framework and taming the conflict inside populations"

Connected through hyperedge HE-4 (induced by source paper "novelty organizing team of classifiers in noisy and dynamic environments"):
  with members:
    - "self organizing classifiers first steps in structured evolutionary machine learning"

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)