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: "the mating rituals of deep neural networks learning compact feature representations through sexual evolutionary synthesis"
  Abstract: Evolutionary deep intelligence was recently proposed as a method for achieving highly efficient deep neural network architectures over successive generations. Drawing inspiration from nature, we propose the incorporation of sexual evolutionary synthesis. Rather than the current asexual synthesis of networks, we aim to produce more compact feature representations by synthesizing more diverse and generalizable offspring networks in subsequent generations via the combination of two parent networks. Experimental results were obtained using the MNIST and CIFAR-10 datasets, and showed improved architectural efficiency and comparable testing accuracy relative to the baseline asexual evolutionary neural networks. In particular, the network synthesized via sexual evolutionary synthesis for MNIST had approximately double the architectural efficiency (cluster efficiency of 34.29X and synaptic efficiency of 258.37X) in comparison to the network synthesized via asexual evolutionary synthesis, with both networks achieving a testing accuracy of ~97%.

Connected through hyperedge HE-1 (induced by source paper "assessing architectural similarity in populations of deep neural networks"):
  with members:
    - "mitigating architectural mismatch during the evolutionary synthesis of deep neural networks"
    - "the microsoft 2017 conversational speech recognition system"
    - "stacked hourglass networks for human pose estimation"
    - "deep learning with darwin evolutionary synthesis of deep neural networks"
    - "evolutionary synthesis of deep neural networks via synaptic cluster driven genetic encoding"
    - "joint training of a convolutional network and a graphical model for human pose estimation"
    - "very deep convolutional networks for large scale image recognition"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "nature vs nurture the role of environmental resources in evolutionary deep intelligence"):
  with members:
    - "evolution in groups a deeper look at synaptic cluster driven evolution of deep neural networks"
    - "exploring the imposition of synaptic precision restrictions for evolutionary synthesis of deep neural networks"
    - "discovery radiomics via evolutionary deep radiomic sequencer discovery for pathologically proven lung cancer detection"
    - "squishednets squishing squeezenet further for edge device scenarios via deep evolutionary synthesis"
    - "deep learning with darwin evolutionary synthesis of deep neural networks"
    - "evolutionary synthesis of deep neural networks via synaptic cluster driven genetic encoding"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "sparsifying neural network connections for face recognition"

Connected through hyperedge HE-3 (induced by source paper "mitigating architectural mismatch during the evolutionary synthesis of deep neural networks"):
  with members:
    - "nature vs nurture the role of environmental resources in evolutionary deep intelligence"
    - "evolution in groups a deeper look at synaptic cluster driven evolution of deep neural networks"
    - "exploring the imposition of synaptic precision restrictions for evolutionary synthesis of deep neural networks"
    - "deep learning with darwin evolutionary synthesis of deep neural networks"
    - "evolutionary synthesis of deep neural networks via synaptic cluster driven genetic encoding"

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)