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 first steps in structured evolutionary machine learning"
  Abstract: Learning classifier systems (LCSs) are evolutionary machine learning algorithms, flexible enough to be applied to reinforcement, supervised and unsupervised learning problems with good performance. Recently, self organizing classifiers were proposed which are similar to LCSs but have the advantage that in its structured population no balance between niching and fitness pressure is necessary. However, more tests and analysis are required to verify its benefits. Here, a variation of the first algorithm is proposed which uses a parameterless self organizing map (SOM). This algorithm is applied in challenging problems such as big, noisy as well as dynamically changing continuous input-action mazes (growing and compressing mazes are included) with good performance. Moreover, a genetic operator is proposed which utilizes the topological information of the SOM's population structure, improving the results. Thus, the first steps in structured evolutionary machine learning are shown, nonetheless, the problems faced are more difficult than the state-of-art continuous input-action multi-step ones.

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"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "on the suitability of l_p norms for creating and preventing adversarial examples"
    - "self organizing classifiers and niched fitness"
    - "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 "robust optimization through neuroevolution"):
  with members:
    - "towards generalization and simplicity in continuous control"
    - "spectrum diverse neuroevolution with unified neural models"
    - "novelty organizing team of classifiers in noisy and dynamic environments"
    - "robots that can adapt like animals"

Connected through hyperedge HE-3 (induced by source paper "novelty organizing team of classifiers in noisy and dynamic environments"):
  with members:
    - "self organizing classifiers and niched fitness"

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)