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: "evolution in groups a deeper look at synaptic cluster driven evolution of deep neural networks"
  Abstract: A promising paradigm for achieving highly efficient deep neural networks is the idea of evolutionary deep intelligence, which mimics biological evolution processes to progressively synthesize more efficient networks. A crucial design factor in evolutionary deep intelligence is the genetic encoding scheme used to simulate heredity and determine the architectures of offspring networks. In this study, we take a deeper look at the notion of synaptic cluster-driven evolution of deep neural networks which guides the evolution process towards the formation of a highly sparse set of synaptic clusters in offspring networks. Utilizing a synaptic cluster-driven genetic encoding, the probabilistic encoding of synaptic traits considers not only individual synaptic properties but also inter-synaptic relationships within a deep neural network. This process results in highly sparse offspring networks which are particularly tailored for parallel computational devices such as GPUs and deep neural network accelerator chips. Comprehensive experimental results using four well-known deep neural network architectures (LeNet-5, AlexNet, ResNet-56, and DetectNet) on two different tasks (object categorization and object detection) demonstrate the efficiency of the proposed method. Cluster-driven genetic encoding scheme synthesizes networks that can achieve state-of-the-art performance with significantly smaller number of synapses than that of the original ancestor network. ($\sim$125-fold decrease in synapses for MNIST). Furthermore, the improved cluster efficiency in the generated offspring networks ($\sim$9.71-fold decrease in clusters for MNIST and a $\sim$8.16-fold decrease in clusters for KITTI) is particularly useful for accelerated performance on parallel computing hardware architectures such as those in GPUs and deep neural network accelerator chips.

Connected through hyperedge HE-1 (induced by source paper "evolutionary construction of convolutional neural networks"):
  with members:
    - "evolving deep neural networks"
    - "large scale evolution of image classifiers"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "evolving parsimonious networks by mixing activation functions"
    - "a genetic programming approach to designing convolutional neural network architectures"
    - "large scale evolution of convolutional neural networks using volunteer computing"
    - "metaheuristic algorithms for convolution neural network"

Connected through hyperedge HE-2 (induced by source paper "stressednets efficient feature representations via stress induced evolutionary synthesis of deep neural networks"):
  with members:
    - "learning structured sparsity in deep neural networks"
    - "yolo9000 better faster stronger"
    - "deep learning with darwin evolutionary synthesis of deep neural networks"
    - "dynamic network surgery for efficient dnns"
    - "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"
    - "training cnns with low rank filters for efficient image classification"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-3 (induced by source paper "nature vs nurture the role of environmental resources in evolutionary deep intelligence"):
  with members:
    - "the mating rituals of deep neural networks learning compact feature representations through sexual evolutionary synthesis"
    - "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-4 (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"
    - "the mating rituals of deep neural networks learning compact feature representations through sexual evolutionary synthesis"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "fast yolo a fast you only look once system for real time embedded object detection in video"):
  with members:
    - "yolo9000 better faster stronger"
    - "deep learning with darwin evolutionary synthesis of deep neural networks"
    - "learning both weights and connections for efficient neural networks"
    - "training cnns with low rank filters for efficient image classification"
    - "compressing neural networks with the hashing trick"
    - "very deep convolutional networks for large scale image recognition"
    - "compressing deep convolutional networks using vector quantization"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "discovery radiomics via evolutionary deep radiomic sequencer discovery for pathologically proven lung cancer detection"):
  with members:
    - "deep learning with darwin evolutionary synthesis of deep neural networks"
    - "evolutionary synthesis of deep neural networks via synaptic cluster driven genetic encoding"
    - "stochasticnet forming deep neural networks via stochastic connectivity"
    - "discovery radiomics via stochasticnet sequencers for cancer detection"

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)