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: "good practice in cnn feature transfer"
  Abstract: The objective of this paper is the effective transfer of the Convolutional Neural Network (CNN) feature in image search and classification. Systematically, we study three facts in CNN transfer. 1) We demonstrate the advantage of using images with a properly large size as input to CNN instead of the conventionally resized one. 2) We benchmark the performance of different CNN layers improved by average/max pooling on the feature maps. Our observation suggests that the Conv5 feature yields very competitive accuracy under such pooling step. 3) We find that the simple combination of pooled features extracted across various CNN layers is effective in collecting evidences from both low and high level descriptors. Following these good practices, we are capable of improving the state of the art on a number of benchmarks to a large margin.

Connected through hyperedge HE-1 (induced by source paper "learning to see across domains and modalities"):
  with members:
    - "boosting domain adaptation by discovering latent domains"
    - "generalizing across domains via cross gradient training"
    - "learning from noisy web data with category level supervision"
    - "kitting in the wild through online domain adaptation"
    - "robust place categorization with deep domain generalization"
    - "deep hashing network for unsupervised domain adaptation"
    - "cycada cycle consistent adversarial domain adaptation"
    - "deep layer aggregation"

Connected through hyperedge HE-2 (induced by source paper "growing a brain fine tuning by increasing model capacity"):
  with members:
    - "knowledge transfer in deep block modular neural networks"
    - "progressive neural networks"
    - "a deep hierarchical approach to lifelong learning in minecraft"
    - "a growing long term episodic semantic memory"
    - "one shot learning with memory augmented neural networks"
    - "learning feed forward one shot learners"
    - "cross stitch networks for multi task learning"
    - "what makes imagenet good for transfer learning"

Connected through hyperedge HE-3 (induced by source paper "visual search over billions of aerial and satellite images"):
  with members:
    - "generalization in metric learning should the embedding layer be the embedding layer"
    - "visual discovery at pinterest"
    - "sift meets cnn a decade survey of instance retrieval"
    - "data intensive supercomputing in the cloud global analytics for satellite imagery"
    - "deep residual learning for image recognition"
    - "particular object retrieval with integral max pooling of cnn activations"
    - "visual search at pinterest"
    - "neural codes for image retrieval"

Connected through hyperedge HE-4 (induced by source paper "component based attention for large scale trademark retrieval"):
  with members:
    - "improved person re identification based on saliency and semantic parsing with deep neural network models"
    - "focal loss for dense object detection"
    - "selective deep convolutional features for image retrieval"
    - "a large scale dataset and benchmark for similar trademark retrieval"
    - "scene text eraser"
    - "selective convolutional descriptor aggregation for fine grained image retrieval"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "detecting text in natural image with connectionist text proposal network"

Connected through hyperedge HE-5 (induced by source paper "instance search via instance level segmentation and feature representation"):
  with members:
    - "deformable convolutional networks"
    - "faster r cnn features for instance search"
    - "aggregated residual transformations for deep neural networks"
    - "fully convolutional instance aware semantic segmentation"
    - "interactive inter layer activeness propagation"
    - "cross dimensional weighting for aggregated deep convolutional features"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "non iterative recomputation of dense layers for performance improvement of dcnn"):
  with members:
    - "places an image database for deep scene understanding"
    - "densely connected convolutional networks"
    - "deep learning in neural networks"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "striving for simplicity the all convolutional net"

Connected through hyperedge HE-7 (induced by source paper "speeding up the hyperparameter optimization of deep convolutional neural networks"):
  with members:
    - "evolving deep neural networks"
    - "learning transferable architectures for scalable image recognition"
    - "efficient architecture search by network transformation"
    - "practical block wise neural network architecture generation"
    - "fast bayesian optimization of machine learning hyperparameters on large datasets"
    - "neural architecture search with reinforcement learning"
    - "do deep neural networks learn facial action units when doing expression recognition"
    - "overfeat integrated recognition localization and detection using convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "fine tuning cnn image retrieval with no human annotation"):
  with members:
    - "siamese network of deep fisher vector descriptors for image retrieval"
    - "end to end learning of deep visual representations for image retrieval"
    - "bags of local convolutional features for scalable instance search"
    - "deep image retrieval learning global representations for image search"
    - "nested invariance pooling and rbm hashing for image instance retrieval"
    - "large scale image retrieval with attentive deep local features"
    - "cnn image retrieval learns from bow unsupervised fine tuning with hard examples"
    - "cross dimensional weighting for aggregated deep convolutional features"

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)