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: "analysis and optimization of convolutional neural network architectures"
  Abstract: Convolutional Neural Networks (CNNs) dominate various computer vision tasks since Alex Krizhevsky showed that they can be trained effectively and reduced the top-5 error from 26.2 % to 15.3 % on the ImageNet large scale visual recognition challenge. Many aspects of CNNs are examined in various publications, but literature about the analysis and construction of neural network architectures is rare. This work is one step to close this gap. A comprehensive overview over existing techniques for CNN analysis and topology construction is provided. A novel way to visualize classification errors with confusion matrices was developed. Based on this method, hierarchical classifiers are described and evaluated. Additionally, some results are confirmed and quantified for CIFAR-100. For example, the positive impact of smaller batch sizes, averaging ensembles, data augmentation and test-time transformations on the accuracy. Other results, such as the positive impact of learned color transformation on the test accuracy could not be confirmed. A model which has only one million learned parameters for an input size of 32x32x3 and 100 classes and which beats the state of the art on the benchmark dataset Asirra, GTSRB, HASYv2 and STL-10 was developed.

Connected through hyperedge HE-1 (induced by source paper "knowledge flow improve upon your teachers"):
  with members:
    - "growing a brain fine tuning by increasing model capacity"
    - "progressive neural networks"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "emnist an extension of mnist to handwritten letters"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "distral robust multitask reinforcement learning"
    - "proximal policy optimization algorithms"
    - "less forgetting learning in deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "style augmentation data augmentation via style randomization"):
  with members:
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "random erasing data augmentation"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "adversarial discriminative domain adaptation"
    - "exploring the structure of a real time arbitrary neural artistic stylization network"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "sparsity invariant cnns"

Connected through hyperedge HE-3 (induced by source paper "fast efficient object detection using selective attention"):
  with members:
    - "tiny ssd a tiny single shot detection deep convolutional neural network for real time embedded object detection"
    - "fusing saliency maps with region proposals for unsupervised object localization"
    - "focal loss for dense object detection"
    - "training region based object detectors with online hard example mining"
    - "yolo9000 better faster stronger"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "beyond skip connections top down modulation for object detection"
    - "what makes for effective detection proposals"

Connected through hyperedge HE-4 (induced by source paper "broadcasting convolutional network for visual relational reasoning"):
  with members:
    - "inferring and executing programs for visual reasoning"
    - "a simple neural network module for relational reasoning"
    - "active convolution learning the shape of convolution for image classification"
    - "deformable convolutional networks"
    - "squeeze and excitation networks"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "film visual reasoning with a general conditioning layer"
    - "discovering objects and their relations from entangled scene representations"

Connected through hyperedge HE-5 (induced by source paper "ol e orthogonal low rank embedding a plug and play geometric loss for deep learning"):
  with members:
    - "deep sparse subspace clustering"
    - "sphereface deep hypersphere embedding for face recognition"
    - "deep subspace clustering networks"
    - "distance based confidence score for neural network classifiers"
    - "identity mappings in deep residual networks"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"

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)