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: "harvesting discriminative meta objects with deep cnn features for scene classification"
  Abstract: Recent work on scene classification still makes use of generic CNN features in a rudimentary manner. In this ICCV 2015 paper, we present a novel pipeline built upon deep CNN features to harvest discriminative visual objects and parts for scene classification. We first use a region proposal technique to generate a set of high-quality patches potentially containing objects, and apply a pre-trained CNN to extract generic deep features from these patches. Then we perform both unsupervised and weakly supervised learning to screen these patches and discover discriminative ones representing category-specific objects and parts. We further apply discriminative clustering enhanced with local CNN fine-tuning to aggregate similar objects and parts into groups, called meta objects. A scene image representation is constructed by pooling the feature response maps of all the learned meta objects at multiple spatial scales. We have confirmed that the scene image representation obtained using this new pipeline is capable of delivering state-of-the-art performance on two popular scene benchmark datasets, MIT Indoor 67~\cite{MITIndoor67} and Sun397~\cite{Sun397}

Connected through hyperedge HE-1 (induced by source paper "inspect transfer learning architecture with dilated convolution"):
  with members:
    - "a survey on deep transfer learning"
    - "autoaugment learning augmentation policies from data"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "drop activation implicit parameter reduction and harmonic regularization"
    - "deformable convolutional networks"
    - "recurrent scene parsing with perspective understanding in the loop"
    - "improved regularization of convolutional neural networks with cutout"

Connected through hyperedge HE-2 (induced by source paper "fosnet an end to end trainable deep neural network for scene recognition"):
  with members:
    - "from volcano to toyshop adaptive discriminative region discovery for scene recognition"
    - "partial convolution based padding"
    - "hybrid cnn and dictionary based models for scene recognition and domain adaptation"
    - "a robust indoor scene recognition method based on sparse representation"
    - "learnable pooling with context gating for video classification"
    - "knowledge guided disambiguation for large scale scene classification with multi resolution cnns"
    - "squeeze and excitation networks"
    - "weakly supervised patchnets describing and aggregating local patches for scene recognition"

Connected through hyperedge HE-3 (induced by source paper "scene recognition with prototype agnostic scene layout"):
  with members:
    - "semantic fisher scores for task transfer using objects to classify scenes"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "from volcano to toyshop adaptive discriminative region discovery for scene recognition"
    - "graph r cnn for scene graph generation"
    - "a deeper look at power normalizations"
    - "situation recognition with graph neural networks"
    - "network dissection quantifying interpretability of deep visual representations"
    - "hybrid cnn and dictionary based models for scene recognition and domain adaptation"

Connected through hyperedge HE-4 (induced by source paper "fraud detection in networks state of the art"):
  with members:
    - "unsupervised dictionary learning for anomaly detection"
    - "spotting collective behaviour of online frauds in customer reviews"
    - "deep learning for anomaly detection a survey"
    - "midas microcluster based detector of anomalies in edge streams"
    - "a deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data"
    - "no place to hide catching fraudulent entities in tensors"
    - "anomaly detection using one class neural networks"
    - "one class adversarial nets for fraud detection"

Connected through hyperedge HE-5 (induced by source paper "semantic fisher scores for task transfer using objects to classify scenes"):
  with members:
    - "locally supervised deep hybrid model for scene recognition"
    - "compositional model based fisher vector coding for image classification"
    - "label embedding for image classification"
    - "deep filter banks for texture recognition description and segmentation"
    - "compact bilinear pooling"
    - "bilinear cnn models for fine grained visual recognition"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-6 (induced by source paper "learning from web data the benefit of unsupervised object localization"):
  with members:
    - "weakly supervised semantic segmentation using web crawled videos"
    - "cascade r cnn delving into high quality object detection"
    - "dsod learning deeply supervised object detectors from scratch"
    - "exploiting web images for dataset construction a domain robust approach"
    - "snn stacked neural networks"
    - "attend in groups a weakly supervised deep learning framework for learning from web data"
    - "yolo9000 better faster stronger"
    - "learning to segment object candidates"

Connected through hyperedge HE-7 (induced by source paper "from volcano to toyshop adaptive discriminative region discovery for scene recognition"):
  with members:
    - "fine grained discriminative localization via saliency guided faster r cnn"
    - "hybrid cnn and dictionary based models for scene recognition and domain adaptation"
    - "scene graph generation by iterative message passing"
    - "knowledge guided disambiguation for large scale scene classification with multi resolution cnns"
    - "weakly supervised patchnets describing and aggregating local patches for scene recognition"
    - "object level context modeling for scene classification with context cnn"
    - "locally supervised deep hybrid model for scene recognition"
    - "scene recognition with cnns objects scales and dataset bias"

Connected through hyperedge HE-8 (induced by source paper "deep attentional structured representation learning for visual recognition"):
  with members:
    - "statistically motivated second order pooling"
    - "improved bilinear pooling with cnns"
    - "mask cnn localizing parts and selecting descriptors for fine grained image recognition"
    - "deep fishernet for object classification"
    - "compact bilinear pooling"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "learning deep features for discriminative localization"
    - "adam a method for stochastic optimization"

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)