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: "significance of softmax based features in comparison to distance metric learning based features"
  Abstract: The extraction of useful deep features is important for many computer vision tasks. Deep features extracted from classification networks have proved to perform well in those tasks. To obtain features of greater usefulness, end-to-end distance metric learning (DML) has been applied to train the feature extractor directly. However, in these DML studies, there were no equitable comparisons between features extracted from a DML-based network and those from a softmax-based network. In this paper, by presenting objective comparisons between these two approaches under the same network architecture, we show that the softmax-based features perform competitive, or even better, to the state-of-the-art DML features when the size of the dataset, that is, the number of training samples per class, is large. The results suggest that softmax-based features should be properly taken into account when evaluating the performance of deep features.

Connected through hyperedge HE-1 (induced by source paper "unsupervised embedding learning via invariant and spreading instance feature"):
  with members:
    - "deep clustering for unsupervised learning of visual features"
    - "mining on manifolds metric learning without labels"
    - "in defense of the triplet loss for person re identification"
    - "scaling the scattering transform deep hybrid networks"
    - "dynamic label graph matching for unsupervised video re identification"
    - "alignedreid surpassing human level performance in person re identification"
    - "no fuss distance metric learning using proxies"
    - "learning spread out local feature descriptors"

Connected through hyperedge HE-2 (induced by source paper "multi representational learning for offline signature verification using multi loss snapshot ensemble of cnns"):
  with members:
    - "active transfer learning for persian offline signature verification"
    - "learning representations from persian handwriting for offline signature verification a deep transfer learning approach"
    - "a writer independent approach for offline signature verification using deep convolutional neural networks features"
    - "fixed sized representation learning from offline handwritten signatures of different sizes"
    - "using mode connectivity for loss landscape analysis"
    - "a comprehensive study of sparse representation techniques for offline signature verification"
    - "offline handwritten signature verification literature review"
    - "in defense of the triplet loss for person re identification"

Connected through hyperedge HE-3 (induced by source paper "hierarchy based image embeddings for semantic image retrieval"):
  with members:
    - "label embedding network learning label representation for soft training of deep networks"
    - "random erasing data augmentation"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "learning structured semantic embeddings for visual recognition"
    - "sgdr stochastic gradient descent with restarts"
    - "yolo9000 better faster stronger"
    - "densely connected convolutional networks"
    - "deep pyramidal residual networks"

Connected through hyperedge HE-4 (induced by source paper "deep learning vs traditional computer vision"):
  with members:
    - "recurrent convolutional fusion for rgb d object recognition"
    - "hybrid bayesian eigenobjects combining linear subspace and deep network methods for 3d robot vision"
    - "hybrid scene compression for visual localization"
    - "pointnetvlad deep point cloud based retrieval for large scale place recognition"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "augmented reality meets computer vision efficient data generation for urban driving scenes"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "the effectiveness of data augmentation in image classification using deep learning"

Connected through hyperedge HE-5 (induced by source paper "enhancing generic segmentation with learned region representations"):
  with members:
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"
    - "convolutional oriented boundaries from image segmentation to high level tasks"
    - "yolo9000 better faster stronger"
    - "pyramid scene parsing network"
    - "holistically nested edge detection"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-6 (induced by source paper "enhancing flood impact analysis using interactive retrieval of social media images"):
  with members:
    - "adaptive co weighting deep convolutional features for object retrieval"
    - "fine tuning cnn image retrieval with no human annotation"
    - "end to end learning of deep visual representations for image retrieval"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "neural codes for image retrieval"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "recognizing variables from their data via deep embeddings of distributions"):
  with members:
    - "neural arithmetic logic units"
    - "deep sets"
    - "multi level variational autoencoder learning disentangled representations from grouped observations"
    - "enriching word vectors with subword information"
    - "facenet a unified embedding for face recognition and clustering"
    - "adam a method for stochastic optimization"
    - "openml networked science in machine learning"

Connected through hyperedge HE-8 (induced by source paper "recognition of multiple food items in a single photo for use in a buffet style restaurant"):
  with members:
    - "wide slice residual networks for food 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)