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: "tiny descriptors for image retrieval with unsupervised triplet hashing"
  Abstract: A typical image retrieval pipeline starts with the comparison of global descriptors from a large database to find a short list of candidate matches. A good image descriptor is key to the retrieval pipeline and should reconcile two contradictory requirements: providing recall rates as high as possible and being as compact as possible for fast matching. Following the recent successes of Deep Convolutional Neural Networks (DCNN) for large scale image classification, descriptors extracted from DCNNs are increasingly used in place of the traditional hand crafted descriptors such as Fisher Vectors (FV) with better retrieval performances. Nevertheless, the dimensionality of a typical DCNN descriptor --extracted either from the visual feature pyramid or the fully-connected layers-- remains quite high at several thousands of scalar values. In this paper, we propose Unsupervised Triplet Hashing (UTH), a fully unsupervised method to compute extremely compact binary hashes --in the 32-256 bits range-- from high-dimensional global descriptors. UTH consists of two successive deep learning steps. First, Stacked Restricted Boltzmann Machines (SRBM), a type of unsupervised deep neural nets, are used to learn binary embedding functions able to bring the descriptor size down to the desired bitrate. SRBMs are typically able to ensure a very high compression rate at the expense of loosing some desirable metric properties of the original DCNN descriptor space. Then, triplet networks, a rank learning scheme based on weight sharing nets is used to fine-tune the binary embedding functions to retain as much as possible of the useful metric properties of the original space. A thorough empirical evaluation conducted on multiple publicly available dataset using DCNN descriptors shows that our method is able to significantly outperform state-of-the-art unsupervised schemes in the target bit range.

Connected through hyperedge HE-1 (induced by source paper "pruning convolutional neural networks for image instance retrieval"):
  with members:
    - "deep image retrieval learning global representations for image search"
    - "pruning convolutional neural networks for resource efficient inference"
    - "group invariant deep representations for image instance retrieval"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "cross dimensional weighting for aggregated deep convolutional features"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "compression of deep convolutional neural networks for fast and low power mobile applications"

Connected through hyperedge HE-2 (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"
    - "deephash getting regularization depth and fine tuning right"
    - "cnn based hashing for image retrieval"
    - "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-3 (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"
    - "cnn based hashing for image retrieval"
    - "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-4 (induced by source paper "group invariant deep representations for image instance retrieval"):
  with members:
    - "deephash getting regularization depth and fine tuning right"
    - "a practical guide to cnns and fisher vectors for image instance retrieval"
    - "very deep convolutional networks for large scale image recognition"
    - "a baseline for visual instance retrieval with deep convolutional networks"
    - "a deep representation for invariance and music classification"
    - "neural codes for image retrieval"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "deeppose human pose estimation via deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "bloom filters and compact hash codes for efficient and distributed image retrieval"):
  with members:
    - "large scale query by image video retrieval using bloom filters"
    - "exploiting local features from deep networks for image retrieval"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "neural codes for image retrieval"

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.IR(Information Retrieval)