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 based hashing for image retrieval"
  Abstract: Along with data on the web increasing dramatically, hashing is becoming more and more popular as a method of approximate nearest neighbor search. Previous supervised hashing methods utilized similarity/dissimilarity matrix to get semantic information. But the matrix is not easy to construct for a new dataset. Rather than to reconstruct the matrix, we proposed a straightforward CNN-based hashing method, i.e. binarilizing the activations of a fully connected layer with threshold 0 and taking the binary result as hash codes. This method achieved the best performance on CIFAR-10 and was comparable with the state-of-the-art on MNIST. And our experiments on CIFAR-10 suggested that the signs of activations may carry more information than the relative values of activations between samples, and that the co-adaption between feature extractor and hash functions is important for hashing.

Connected through hyperedge HE-1 (induced by source paper "the case for learned index structures"):
  with members:
    - "a model for learned bloom filters and related structures"
    - "a tree a bounded approximate index structure"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "imagenet training in minutes"
    - "the end of a myth distributed transactions can scale"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "tensorflow a system for large scale machine learning"
    - "learning to hash for indexing big data a survey"

Connected through hyperedge HE-2 (induced by source paper "transductive zero shot hashing via coarse to fine similarity mining"):
  with members:
    - "deep hashing network for unsupervised domain adaptation"
    - "hashing in the zero shot framework with domain adaptation"
    - "deep sketch hashing fast free hand sketch based image retrieval"
    - "unsupervised deep hashing for large scale visual search"
    - "fast training of triplet based deep binary embedding networks"
    - "deep cross modal hashing"
    - "transductive multi view zero shot learning"
    - "efficient training of very deep neural networks for supervised hashing"

Connected through hyperedge HE-3 (induced by source paper "unsupervised triplet hashing for fast image retrieval"):
  with members:
    - "cnn image retrieval learns from bow unsupervised fine tuning with hard examples"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "tiny descriptors for image retrieval with unsupervised triplet hashing"
    - "deephash getting regularization depth and fine tuning right"
    - "how transferable are features in deep neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-4 (induced by source paper "compact hash codes for efficient visual descriptors retrieval in large scale databases"):
  with members:
    - "deep image retrieval learning global representations for image search"
    - "supervised hashing with deep neural networks"
    - "tiny descriptors for image retrieval with unsupervised triplet hashing"
    - "discrete hashing with deep neural network"
    - "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"
    - "packing and padding coupled multi index for accurate image retrieval"

Connected through hyperedge HE-5 (induced by source paper "binary codes for tagging x ray images via deep de noising autoencoders"):
  with members:
    - "tumour roi estimation in ultrasound images via radon barcodes in patients with locally advanced breast cancer"
    - "barcode annotations for medical image retrieval a preliminary investigation"
    - "feature learning based deep supervised hashing with pairwise labels"
    - "medical image classification via svm using lbp features from saliency based folded data"
    - "autoencoding the retrieval relevance of medical images"
    - "bit scalable deep hashing with regularized similarity learning for image retrieval and person re identification"
    - "adam a method for stochastic optimization"
    - "theano new features and speed improvements"

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)