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: "evolutionary synthesis of deep neural networks via synaptic cluster driven genetic encoding"
  Abstract: There has been significant recent interest towards achieving highly efficient deep neural network architectures. A promising paradigm for achieving this is the concept of evolutionary deep intelligence, which attempts to mimic biological evolution processes to synthesize highly-efficient deep neural networks over successive generations. An important aspect of evolutionary deep intelligence is the genetic encoding scheme used to mimic heredity, which can have a significant impact on the quality of offspring deep neural networks. Motivated by the neurobiological phenomenon of synaptic clustering, we introduce a new genetic encoding scheme where synaptic probability is driven towards the formation of a highly sparse set of synaptic clusters. Experimental results for the task of image classification demonstrated that the synthesized offspring networks using this synaptic cluster-driven genetic encoding scheme can achieve state-of-the-art performance while having network architectures that are not only significantly more efficient (with a ~125-fold decrease in synapses for MNIST) compared to the original ancestor network, but also tailored for GPU-accelerated machine learning applications.

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 mating rituals of deep neural networks learning compact feature representations through sexual evolutionary synthesis"
    - "the microsoft 2017 conversational speech recognition system"
    - "stacked hourglass networks for human pose estimation"
    - "deep learning with darwin evolutionary synthesis of deep neural networks"
    - "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 "dynamic representations toward efficient inference on deep neural networks by decision gates"):
  with members:
    - "label refinery improving imagenet classification through label progression"
    - "dynamic deep neural networks optimizing accuracy efficiency trade offs by selective execution"
    - "learning transferable architectures for scalable image recognition"
    - "adaptive neural networks for efficient inference"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "blockdrop dynamic inference paths in residual networks"
    - "branchynet fast inference via early exiting from deep neural networks"
    - "mask r cnn"

Connected through hyperedge HE-3 (induced by source paper "stressednets efficient feature representations via stress induced evolutionary synthesis of deep neural networks"):
  with members:
    - "evolution in groups a deeper look at synaptic cluster driven evolution of deep neural networks"
    - "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"
    - "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-4 (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"
    - "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"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "sparsifying neural network connections for face recognition"

Connected through hyperedge HE-5 (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"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "the mating rituals of deep neural networks learning compact feature representations through sexual evolutionary synthesis"):
  with members:
    - "learning structured sparsity in deep neural networks"
    - "deep learning with darwin evolutionary synthesis of deep neural networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "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"
    - "joint training of a convolutional network and a graphical model for human pose estimation"
    - "speeding up convolutional neural networks with low rank expansions"

Connected through hyperedge HE-7 (induced by source paper "squishednets squishing squeezenet further for edge device scenarios via deep evolutionary synthesis"):
  with members:
    - "exploring the imposition of synaptic precision restrictions for evolutionary synthesis of deep neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "fixed point optimization of deep neural networks with adaptive step size retraining"
    - "two bit networks for deep learning on resource constrained embedded devices"
    - "quantization and training of low bit width convolutional neural networks for object detection"
    - "ternary weight networks"
    - "deep learning with darwin evolutionary synthesis of deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "discovery radiomics via evolutionary deep radiomic sequencer discovery for pathologically proven lung cancer detection"):
  with members:
    - "evolution in groups a deeper look at synaptic cluster driven evolution of deep neural networks"
    - "deep learning with darwin evolutionary synthesis of deep neural networks"
    - "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.LG(Machine Learning)