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: "cnn features are also great at unsupervised classification"
  Abstract: This paper aims at providing insight on the transferability of deep CNN features to unsupervised problems. We study the impact of different pretrained CNN feature extractors on the problem of image set clustering for object classification as well as fine-grained classification. We propose a rather straightforward pipeline combining deep-feature extraction using a CNN pretrained on ImageNet and a classic clustering algorithm to classify sets of images. This approach is compared to state-of-the-art algorithms in image-clustering and provides better results. These results strengthen the belief that supervised training of deep CNN on large datasets, with a large variability of classes, extracts better features than most carefully designed engineering approaches, even for unsupervised tasks. We also validate our approach on a robotic application, consisting in sorting and storing objects smartly based on clustering.

Connected through hyperedge HE-1 (induced by source paper "uncertainty quantification for deep context aware mobile activity recognition and unknown context discovery"):
  with members:
    - "adaptive activity monitoring with uncertainty quantification in switching gaussian process models"
    - "deep spatially coherent inverse sensor models with uncertainty incorporation using the evidential framework"
    - "deep learning for sensor based activity recognition a survey"
    - "evidential deep learning to quantify classification uncertainty"
    - "learnable pooling with context gating for video classification"
    - "expert gate lifelong learning with a network of experts"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "decaf a deep convolutional activation feature for generic visual recognition"

Connected through hyperedge HE-2 (induced by source paper "collaborative learning of semi supervised clustering and classification for labeling uncurated data"):
  with members:
    - "an analytical workflow for clustering forensic images"
    - "hard sample mining for the improved retraining of automatic speech recognition"
    - "exploring the limits of weakly supervised pretraining"
    - "unsupervised joint mining of deep features and image labels for large scale radiology image categorization and scene recognition"
    - "photo guided exploration of volume data features"
    - "joint unsupervised learning of deep representations and image clusters"

Connected through hyperedge HE-3 (induced by source paper "multidec multi modal clustering of image caption pairs"):
  with members:
    - "cross modal retrieval in the cooking context learning semantic text image embeddings"
    - "deep clustering for unsupervised learning of visual features"
    - "improving image clustering with multiple pretrained cnn feature extractors"
    - "end to end cross modality retrieval with cca projections and pairwise ranking loss"
    - "learning robust visual semantic embeddings"
    - "ezlearn exploiting organic supervision in large scale data annotation"
    - "deep clustering via joint convolutional autoencoder embedding and relative entropy minimization"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-4 (induced by source paper "video face clustering with unknown number of clusters"):
  with members:
    - "centroid based deep metric learning for speaker recognition"
    - "self supervised learning of face representations for video face clustering"
    - "learnable pins cross modal embeddings for person identity"
    - "tvqa localized compositional video question answering"
    - "from benedict cumberbatch to sherlock holmes character identification in tv series without a script"
    - "normface l 2 hypersphere embedding for face verification"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "generating descriptions with grounded and co referenced people"

Connected through hyperedge HE-5 (induced by source paper "transfer learning from an auxiliary discriminative task for unsupervised anomaly detection"):
  with members:
    - "efficient gan based anomaly detection"
    - "high order graph convolutional recurrent neural network a deep learning framework for network scale traffic learning and forecasting"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "learning representations for automatic colorization"
    - "global multi layer network of human mobility"
    - "deep spatio temporal residual networks for citywide crowd flows prediction"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "jointly discovering visual objects and spoken words from raw sensory input"):
  with members:
    - "cognitive science in the era of artificial intelligence a roadmap for reverse engineering the infant language learner"
    - "visually grounded learning of keyword prediction from untranscribed speech"
    - "learning word like units from joint audio visual analysis"
    - "unsupervised learning of semantic audio representations"
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"
    - "look listen and learn"
    - "vse improving visual semantic embeddings with hard negatives"
    - "encoding of phonology in a recurrent neural model of grounded speech"

Connected through hyperedge HE-7 (induced by source paper "improving image clustering with multiple pretrained cnn feature extractors"):
  with members:
    - "clustering with deep learning taxonomy and new methods"
    - "unsupervised joint mining of deep features and image labels for large scale radiology image categorization and scene recognition"
    - "a survey on multi view clustering"
    - "iterative views agreement an iterative low rank based structured optimization method to multi view spectral clustering"
    - "xception deep learning with depthwise separable convolutions"
    - "joint unsupervised learning of deep representations and image clusters"
    - "deep residual learning for image recognition"
    - "unsupervised deep embedding for clustering analysis"

Connected through hyperedge HE-8 (induced by source paper "pilot comparative study of different deep features for palmprint identification in low quality images"):
  with members:
    - "an analysis of deep neural network models for practical applications"
    - "an experimental study of deep convolutional features for iris recognition"
    - "a comparative study of cnn bovw and lbp for classification of histopathological images"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "from facial parts responses to face detection a deep learning approach"
    - "very deep convolutional networks for large scale image recognition"
    - "on the power of joint wavelet dct features for multispectral palmprint recognition"
    - "cnn features off the shelf an astounding baseline for 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)