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: "spectrum diverse neuroevolution with unified neural models"
  Abstract: Learning algorithms are being increasingly adopted in various applications. However, further expansion will require methods that work more automatically. To enable this level of automation, a more powerful solution representation is needed. However, by increasing the representation complexity, a second problem arises. The search space becomes huge, and therefore, an associated scalable and efficient searching algorithm is also required. To solve both the problems, first a powerful representation is proposed that unifies most of the neural networks features from the literature into one representation. Second, a new diversity preserving method called spectrum diversity is created based on the new concept of chromosome spectrum that creates a spectrum out of the characteristics and frequency of alleles in a chromosome. The combination of spectrum diversity with a unified neuron representation enables the algorithm to either surpass or equal NeuroEvolution of Augmenting Topologies on all of the five classes of problems tested. Ablation tests justify the good results, showing the importance of added new features in the unified neuron representation. Part of the success is attributed to the novelty-focused evolution and good scalability with a chromosome size provided by spectrum diversity. Thus, this paper sheds light on a new representation and diversity preserving mechanism that should impact algorithms and applications to come.

Connected through hyperedge HE-1 (induced by source paper "evolving robust neural architectures to defend from adversarial attacks"):
  with members:
    - "evolving deep neural networks by multi objective particle swarm optimization for image classification"
    - "one pixel attack for fooling deep neural networks"
    - "evolutionary neural automl for deep learning"
    - "understanding the one pixel attack propagation maps and locality analysis"
    - "model agnostic dual quality assessment for adversarial machine learning and an analysis of current neural networks and defenses"
    - "adversarial patch"
    - "evolutionary architecture search for deep multitask networks"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"

Connected through hyperedge HE-2 (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"
    - "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"

Connected through hyperedge HE-3 (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"
    - "self organizing classifiers and niched fitness"
    - "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-4 (induced by source paper "adaptive genomic evolution of neural network topologies agent for state to action mapping in autonomous agents"):
  with members:
    - "advances in experience replay"
    - "multi criteria evolution of neural network topologies balancing experience and performance in autonomous systems"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "the effects of memory replay in reinforcement learning"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "actor critic versus direct policy search a comparison based on sample complexity"
    - "continuous control with deep reinforcement learning"
    - "neuroevolution in games state of the art and open challenges"

Connected through hyperedge HE-5 (induced by source paper "robust optimization through neuroevolution"):
  with members:
    - "self organizing classifiers first steps in structured evolutionary machine learning"
    - "towards generalization and simplicity in continuous control"
    - "novelty organizing team of classifiers in noisy and dynamic environments"
    - "robots that can adapt like animals"

Connected through hyperedge HE-6 (induced by source paper "attacking convolutional neural network using differential evolution"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "deep text classification can be fooled"
    - "audio adversarial examples targeted attacks on speech to text"
    - "classification regions of deep neural networks"
    - "analysis of universal adversarial perturbations"
    - "evading classifiers by morphing in the dark"
    - "practical black box attacks against machine learning"
    - "adversarial diversity and hard positive generation"

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)