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 convolutional neural network design patterns"
  Abstract: Recent research in the deep learning field has produced a plethora of new architectures.  At the same time, a growing number of groups are applying deep learning to new applications.  Some of these groups are likely to be composed of inexperienced deep learning practitioners who are baffled by the dizzying array of architecture choices and therefore opt to use an older architecture (i.e., Alexnet).  Here we attempt to bridge this gap by mining the collective knowledge contained in recent deep learning research to discover underlying principles for designing neural network architectures.  In addition, we describe several architectural innovations, including Fractal of FractalNet network, Stagewise Boosting Networks, and Taylor Series Networks (our Caffe code and prototxt files are available at https://github.com/iPhysicist/CNNDesignPatterns). We hope others are inspired to build on our preliminary work.

Connected through hyperedge HE-1 (induced by source paper "assisting human experts in the interpretation of their visual process a case study on assessing copper surface adhesive potency"):
  with members:
    - "boosting segmentation with weak supervision from image to image translation"
    - "a multi task u net for segmentation with lazy labels"
    - "knowing where to look analysis on attention of visual question answering system"
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "attentive explanations justifying decisions and pointing to the evidence"
    - "image captioning with semantic attention"
    - "making the v in vqa matter elevating the role of image understanding in visual question answering"
    - "understanding neural networks through deep visualization"

Connected through hyperedge HE-2 (induced by source paper "studying software engineering patterns for designing machine learning systems"):
  with members:
    - "landscape of iot patterns"
    - "continuous integration of machine learning models with ease ml ci towards a rigorous yet practical treatment"
    - "machine learning software engineering in practice an industrial case study"

Connected through hyperedge HE-3 (induced by source paper "a comprehensive analysis of deep regression"):
  with members:
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "lstm a search space odyssey"
    - "learning feature pyramids for human pose estimation"
    - "viraliency pooling local virality"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "robust head pose estimation based on partially latent mixture of linear regressions"
    - "adversarial posenet a structure aware convolutional network for human pose estimation"
    - "on architectural choices in deep learning from network structure to gradient convergence and parameter estimation"

Connected through hyperedge HE-4 (induced by source paper "neuron segmentation using deep complete bipartite networks"):
  with members:
    - "bottom up instance segmentation using deep higher order crfs"
    - "identity mappings in deep residual networks"
    - "densely connected convolutional networks"
    - "dcan deep contour aware networks for accurate gland segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "recurrent instance segmentation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "deep convolutional neural networks with merge and run mappings"):
  with members:
    - "residual networks of residual networks multilevel residual networks"
    - "residual networks are exponential ensembles of relatively shallow networks"
    - "multi residual networks"
    - "resnet in resnet generalizing residual architectures"
    - "sgdr stochastic gradient descent with restarts"
    - "aggregated residual transformations for deep neural networks"
    - "deep networks with stochastic depth"
    - "inception v4 inception resnet and the impact of residual connections on learning"

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)