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: "discovering the elite hypervolume by leveraging interspecies correlation"
  Abstract: Evolution has produced an astonishing diversity of species, each filling a different niche. Algorithms like MAP-Elites mimic this divergent evolutionary process to find a set of behaviorally diverse but high-performing solutions, called the elites. Our key insight is that species in nature often share a surprisingly large part of their genome, in spite of occupying very different niches; similarly the elites are likely to be concentrated in a specific "elite hypervolume" whose shape is defined by their common features. In this paper, we first introduce the elite hypervolume concept and propose two metrics to characterize it: the genotypic spread and the genotypic similarity. We then introduce a new variation operator, called "directional variation", that exploits interspecies (or inter-elites) correlations to accelerate the MAP-Elites algorithm. We demonstrate the effectiveness of this operator in three problems (a toy function, a redundant robotic arm, and a hexapod robot).

Connected through hyperedge HE-1 (induced by source paper "automating representation discovery with map elites"):
  with members:
    - "covariance matrix adaptation for the rapid illumination of behavior space"
    - "mapping hearthstone deck spaces through map elites with sliding boundaries"
    - "autonomous skill discovery with quality diversity and unsupervised descriptors"
    - "evolving mario levels in the latent space of a deep convolutional generative adversarial network"
    - "deep interactive evolution"
    - "isolating sources of disentanglement in variational autoencoders"
    - "evolving deep neural networks"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "quality diversity for multi task optimization"):
  with members:
    - "adaptive prior selection for repertoire based online adaptation in robotics"
    - "procedural content generation through quality diversity"
    - "reset free trial and error learning for robot damage recovery"
    - "bayesian optimization with automatic prior selection for data efficient direct policy search"
    - "evolutionary multitasking for single objective continuous optimization benchmark problems performance metric and baseline results"
    - "data efficient exploration optimization and modeling of diverse designs through surrogate assisted illumination"
    - "quality and diversity optimization a unifying modular framework"
    - "robots that can adapt like animals"

Connected through hyperedge HE-3 (induced by source paper "evaluating map elites on constrained optimization problems"):
  with members:
    - "benchmarking evolutionary algorithms for single objective real valued constrained optimization a critical review"
    - "optimisation and illumination of a real world workforce scheduling and routing application via map elites"
    - "enhanced optimization with composite objectives and novelty selection"
    - "multi objective analysis of map elites performance"
    - "quality and diversity optimization a unifying modular framework"
    - "memetic viability evolution for constrained optimization"
    - "robots that can adapt like animals"
    - "illuminating search spaces by mapping elites"

Connected through hyperedge HE-4 (induced by source paper "modeling user selection in quality diversity"):
  with members:
    - "prototype discovery using quality diversity"
    - "quality and diversity optimization a unifying modular framework"
    - "robots that can adapt like animals"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"

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)