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: "deep learning with darwin evolutionary synthesis of deep neural networks"
  Abstract: Taking inspiration from biological evolution, we explore the idea of "Can deep neural networks evolve naturally over successive generations into highly efficient deep neural networks?" by introducing the notion of synthesizing new highly efficient, yet powerful deep neural networks over successive generations via an evolutionary process from ancestor deep neural networks. The architectural traits of ancestor deep neural networks are encoded using synaptic probability models, which can be viewed as the `DNA' of these networks. New descendant networks with differing network architectures are synthesized based on these synaptic probability models from the ancestor networks and computational environmental factor models, in a random manner to mimic heredity, natural selection, and random mutation. These offspring networks are then trained into fully functional networks, like one would train a newborn, and have more efficient, more diverse network architectures than their ancestor networks, while achieving powerful modeling capabilities. Experimental results for the task of visual saliency demonstrated that the synthesized `evolved' offspring networks can achieve state-of-the-art performance while having network architectures that are significantly more efficient (with a staggering $\sim$48-fold decrease in synapses by the fourth generation) compared to the original ancestor network.

Connected through hyperedge HE-1 (induced by source paper "seeing convolution through the eyes of finite transformation semigroup theory an abstract algebraic interpretation of convolutional neural networks"):
  with members:
    - "attonets compact and efficient deep neural networks for the edge via human machine collaborative design"
    - "information theoretic interpretation of deep learning"
    - "ferminets learning generative machines to generate efficient neural networks via generative synthesis"
    - "rotation equivariant cnns for digital pathology"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "opening the black box of deep neural networks via information"

Connected through hyperedge HE-2 (induced by source paper "deeplabnet end to end learning of deep radial basis networks with fully learnable basis functions"):
  with members:
    - "adaptive blending units trainable activation functions for deep neural networks"
    - "deep rbf networks revisited robust classification with rejection"
    - "deep rbfnet point cloud feature learning using radial basis functions"
    - "ferminets learning generative machines to generate efficient neural networks via generative synthesis"
    - "squeeze and excitation networks"
    - "searching for activation functions"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "neural architecture search with reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "deep demosaicing for edge implementation"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "efficient neural architecture search via parameter sharing"
    - "learning deep convolutional networks for demosaicing"
    - "learning transferable architectures for scalable image recognition"
    - "sgdr stochastic gradient descent with restarts"
    - "eie efficient inference engine on compressed deep neural network"
    - "neural architecture search with reinforcement learning"

Connected through hyperedge HE-4 (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"
    - "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-5 (induced by source paper "state of compact architecture search for deep neural networks"):
  with members:
    - "ferminets learning generative machines to generate efficient neural networks via generative synthesis"
    - "morphnet fast simple resource constrained structure learning of deep networks"
    - "learning structured sparsity in deep neural networks"
    - "designing neural network architectures using reinforcement learning"

Connected through hyperedge HE-6 (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-7 (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"
    - "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-8 (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"
    - "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"

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.CV(Computer Vision and Pattern Recognition)