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: "a survey of the recent architectures of deep convolutional neural networks"
  Abstract: Deep Convolutional Neural Networks (CNNs) are a special type of Neural Networks, which have shown exemplary performance on several competitions related to Computer Vision and Image Processing. Interesting application areas of CNN include Image Classification and Segmentation, Object Detection, Video Processing, Natural Language Processing, Speech Recognition, etc. The powerful learning ability of deep CNN is largely due to the use of multiple feature extraction stages that can automatically learn representations from the data. Availability of a large amount of data and improvements in the hardware technology have accelerated the research in CNNs, and recently very interesting deep CNN architectures have been reported. In fact, several interesting ideas to bring advancements in CNNs have been explored such as the use of different activation and loss functions, parameter optimization, regularization, and architectural innovations. However, the major improvement in representational capacity of the deep CNN is achieved through architectural innovations. Especially, the idea of exploiting spatial and channel information, depth and width of architecture, and multi-path information processing has gained substantial attention. Similarly, the idea of using a block of layers as a structural unit is also gaining popularity. This survey thus focuses on the intrinsic taxonomy present in the recently reported deep CNN architectures and consequently, classifies the recent innovations in CNN architectures into seven different categories. These seven categories are based on spatial exploitation, depth, multi-path, width, feature-map exploitation, channel boosting, and attention. Additionally, the elementary understanding of CNN components, current challenges and applications of CNN are also provided.

Connected through hyperedge HE-1 (induced by source paper "a precisely xtreme multi channel hybrid approach for roman urdu sentiment analysis"):
  with members:
    - "from web crawled text to project descriptions automatic summarizing of social innovation projects"
    - "an analysis of hierarchical text classification using word embeddings"
    - "character n gram embeddings to improve rnn language models"
    - "word class embeddings for multiclass text classification"
    - "using text analysis to quantify the similarity and evolution of scientific disciplines"
    - "sentiment classification of customer s reviews about automobiles in roman urdu"
    - "dynamic word embeddings for evolving semantic discovery"
    - "training of deep neural networks based on distance measures using rmsprop"

Connected through hyperedge HE-2 (induced by source paper "neural network representation control gaussian isolation machines and cvc regularization"):
  with members:
    - "activation functions comparison of trends in practice and research for deep learning"
    - "anomaly detection using one class neural networks"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "robust deep and inductive anomaly detection"
    - "generic black box end to end attack against state of the art api call based malware classifiers"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-3 (induced by source paper "self supervised visual feature learning with curriculum"):
  with members:
    - "self supervised visual feature learning with deep neural networks a survey"
    - "advancements in image classification using convolutional neural network"
    - "unsupervised representation learning by predicting image rotations"
    - "automated curriculum learning for neural networks"
    - "cassl curriculum accelerated self supervised learning"
    - "learning representations for automatic colorization"
    - "unsupervised learning of visual representations by solving jigsaw puzzles"
    - "unsupervised visual representation learning by context prediction"

Connected through hyperedge HE-4 (induced by source paper "deep object detection based mitosis analysis in breast cancer histopathological images"):
  with members:
    - "multi stream faster rcnn for mitosis counting in breast cancer images"
    - "mask r cnn"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "densely connected convolutional networks"
    - "feature pyramid networks for object detection"
    - "a unified framework for tumor proliferation score prediction in breast histopathology"
    - "deep residual learning for image recognition"
    - "fast r cnn"

Connected through hyperedge HE-5 (induced by source paper "ties temporal interaction embeddings for enhancing social media integrity at facebook"):
  with members:
    - "predicting dynamic embedding trajectory in temporal interaction networks"
    - "pytorch biggraph a large scale graph embedding system"
    - "poised spotting twitter spam off the beaten paths"
    - "deep learning for hate speech detection in tweets"
    - "attention is all you need"
    - "in a world that counts clustering and detecting fake social engagement at scale"
    - "a critical review of recurrent neural networks for sequence learning"

Connected through hyperedge HE-6 (induced by source paper "angular learning toward discriminative embedded features"):
  with members:
    - "adacos adaptively scaling cosine logits for effectively learning deep face representations"
    - "ensemble soft margin softmax loss for image classification"
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "additive margin softmax for face verification"
    - "sphereface deep hypersphere embedding for face recognition"
    - "one shot face recognition by promoting underrepresented classes"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-7 (induced by source paper "ransomware analysis using feature engineering and deep neural networks"):
  with members:
    - "a recent survey on the applications of genetic programming in image processing"
    - "a new channel boosted convolution neural network using transfer learning"
    - "software defined networking based crypto ransomware detection using http traffic characteristics"
    - "automated behavioral analysis of malware a case study of wannacry ransomware"
    - "automated dynamic analysis of ransomware benefits limitations and use for detection"
    - "deep neural network based malware detection using two dimensional binary program features"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "oculum afficit ocular affect recognition"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "identity free facial expression recognition using conditional generative adversarial network"
    - "deep facial expression recognition a survey"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image 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)