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: "deep convolutional features for image based retrieval and scene categorization"
  Abstract: Several recent approaches showed how the representations learned by Convolutional Neural Networks can be repurposed for novel tasks. Most commonly it has been shown that the activation features of the last fully connected layers (fc7 or fc6) of the network, followed by a linear classifier outperform the state-of-the-art on several recognition challenge datasets. Instead of recognition, this paper focuses on the image retrieval problem and proposes a examines alternative pooling strategies derived for CNN features. The presented scheme uses the features maps from an earlier layer 5 of the CNN architecture, which has been shown to preserve coarse spatial information and is semantically meaningful. We examine several pooling strategies and demonstrate superior performance on the image retrieval task (INRIA Holidays) at the fraction of the computational cost, while using a relatively small memory requirements. In addition to retrieval, we see similar efficiency gains on the SUN397 scene categorization dataset, demonstrating wide applicability of this simple strategy. We also introduce and evaluate a novel GeoPlaces5K dataset from different geographical locations in the world for image retrieval that stresses more dramatic changes in appearance and viewpoint.

Connected through hyperedge HE-1 (induced by source paper "mildnet a lightweight single scaled deep ranking architecture"):
  with members:
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "cross domain image retrieval with a dual attribute aware ranking network"
    - "facenet a unified embedding for face recognition and clustering"
    - "exploiting local features from deep networks for image retrieval"
    - "aggregating deep convolutional features for image retrieval"
    - "very deep convolutional networks for large scale image recognition"
    - "visual instance retrieval with deep convolutional networks"

Connected through hyperedge HE-2 (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"
    - "good practice in cnn feature transfer"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "exploiting deep features for remote sensing image retrieval a systematic investigation"):
  with members:
    - "patternnet a benchmark dataset for performance evaluation of remote sensing image retrieval"
    - "texture characterization using shape co occurrence patterns"
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "learning low dimensional convolutional neural networks for high resolution remote sensing image retrieval"
    - "deep image retrieval learning global representations for image search"
    - "visual descriptors for content based retrieval of remote sensing images"
    - "distributed mining of large scale remote sensing image archives on public computing infrastructures"
    - "cross dimensional weighting for aggregated deep convolutional features"

Connected through hyperedge HE-4 (induced by source paper "full network embedding in a multimodal embedding pipeline"):
  with members:
    - "an out of the box full network embedding for convolutional neural networks"
    - "on the behavior of convolutional nets for feature extraction"
    - "word2visualvec image and video to sentence matching by visual feature prediction"
    - "image captioning with deep bidirectional lstms"
    - "multimodal convolutional neural networks for matching image and sentence"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "unifying visual semantic embeddings with multimodal neural language models"

Connected through hyperedge HE-5 (induced by source paper "building graph representations of deep vector embeddings"):
  with members:
    - "an out of the box full network embedding for convolutional neural networks"
    - "fluid communities a competitive scalable and diverse community detection algorithm"
    - "on the behavior of convolutional nets for feature extraction"
    - "borrowing treasures from the wealthy deep transfer learning through selective joint fine tuning"
    - "deepfood deep learning based food image recognition for computer aided dietary assessment"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "deepwalk online learning of social representations"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "an out of the box full network embedding for convolutional neural networks"):
  with members:
    - "on the behavior of convolutional nets for feature extraction"
    - "borrowing treasures from the wealthy deep transfer learning through selective joint fine tuning"
    - "places an image database for deep scene understanding"
    - "learning transferable features with deep adaptation networks"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "very deep convolutional networks for large scale image recognition"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"

Connected through hyperedge HE-7 (induced by source paper "good practice in cnn feature transfer"):
  with members:
    - "exploiting local features from deep networks for image retrieval"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "a baseline for visual instance retrieval with deep convolutional networks"
    - "hypercolumns for object segmentation and fine grained localization"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "generalized max pooling"
    - "the application of two level attention models in deep convolutional neural network for fine grained image classification"

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)