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 supervised hashing with triplet labels"
  Abstract: Hashing is one of the most popular and powerful approximate nearest neighbor search techniques for large-scale image retrieval. Most traditional hashing methods first represent images as off-the-shelf visual features and then produce hashing codes in a separate stage. However, off-the-shelf visual features may not be optimally compatible with the hash code learning procedure, which may result in sub-optimal hash codes. Recently, deep hashing methods have been proposed to simultaneously learn image features and hash codes using deep neural networks and have shown superior performance over traditional hashing methods. Most deep hashing methods are given supervised information in the form of pairwise labels or triplet labels. The current state-of-the-art deep hashing method DPSH~\cite{li2015feature}, which is based on pairwise labels, performs image feature learning and hash code learning simultaneously by maximizing the likelihood of pairwise similarities. Inspired by DPSH~\cite{li2015feature}, we propose a triplet label based deep hashing method which aims to maximize the likelihood of the given triplet labels. Experimental results show that our method outperforms all the baselines on CIFAR-10 and NUS-WIDE datasets, including the state-of-the-art method DPSH~\cite{li2015feature} and all the previous triplet label based deep hashing methods.

Connected through hyperedge HE-1 (induced by source paper "a survey on deep hashing methods"):
  with members:
    - "end to end supervised product quantization for image search and retrieval"
    - "distillhash unsupervised deep hashing by distilling data pairs"
    - "deep triplet quantization"
    - "hadamard codebook based deep hashing"
    - "central similarity hashing via hadamard matrix"
    - "deep attention guided hashing"
    - "deep spherical quantization for image search"
    - "deep ordinal hashing with spatial attention"

Connected through hyperedge HE-2 (induced by source paper "deep heterogeneous hashing for face video retrieval"):
  with members:
    - "the do s and don ts for cnn based face verification"
    - "is second order information helpful for large scale visual recognition"
    - "l2 constrained softmax loss for discriminative face verification"
    - "hashnet deep learning to hash by continuation"
    - "fast training of triplet based deep binary embedding networks"
    - "deep cross modal hashing"
    - "a riemannian network for spd matrix learning"
    - "umdfaces an annotated face dataset for training deep networks"

Connected through hyperedge HE-3 (induced by source paper "end to end supervised product quantization for image search and retrieval"):
  with members:
    - "learning hash codes via hamming distance targets"
    - "deep supervised discrete hashing"
    - "billion scale similarity search with gpus"
    - "hashnet deep learning to hash by continuation"
    - "deep image retrieval learning global representations for image search"
    - "identity mappings in deep residual networks"
    - "learning to hash with binary deep neural network"
    - "efficient training of very deep neural networks for supervised hashing"

Connected through hyperedge HE-4 (induced by source paper "signal to noise ratio a robust distance metric for deep metric learning"):
  with members:
    - "hybrid attention based decoupled metric learning for zero shot image retrieval"
    - "energy confused adversarial metric learning for zero shot image retrieval and clustering"
    - "sketchmate deep hashing for million scale human sketch retrieval"
    - "almn deep embedding learning with geometrical virtual point generating"
    - "deep hashing network for unsupervised domain adaptation"
    - "deep metric learning with angular loss"
    - "deep sketch hashing fast free hand sketch based image retrieval"
    - "training region based object detectors with online hard example mining"

Connected through hyperedge HE-5 (induced by source paper "accurate visual localization for automotive applications"):
  with members:
    - "deep triplet quantization"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "attention based pyramid aggregation network for visual place recognition"
    - "fine tuning cnn image retrieval with no human annotation"
    - "super convergence very fast training of residual networks using large learning rates"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"
    - "undeepvo monocular visual odometry through unsupervised deep learning"

Connected through hyperedge HE-6 (induced by source paper "deep supervised hashing leveraging quadratic spherical mutual information for content based image retrieval"):
  with members:
    - "triplet based deep hashing network for cross modal retrieval"
    - "mihash online hashing with mutual information"
    - "deep supervised discrete hashing"
    - "asymmetric deep supervised hashing"
    - "hashnet deep learning to hash by continuation"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "densely connected convolutional networks"
    - "a survey on learning to hash"

Connected through hyperedge HE-7 (induced by source paper "triplet based deep hashing network for cross modal retrieval"):
  with members:
    - "query adaptive hash code ranking for large scale multi view visual search"
    - "an overview of cross media retrieval concepts methodologies benchmarks and challenges"
    - "fast training of triplet based deep binary embedding networks"
    - "deep cross modal hashing"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "bit scalable deep hashing with regularized similarity learning for image retrieval and person re identification"
    - "going deeper with convolutions"

Connected through hyperedge HE-8 (induced by source paper "push for quantization deep fisher hashing"):
  with members:
    - "fast supervised discrete hashing"
    - "deep supervised discrete hashing"
    - "hashnet deep learning to hash by continuation"
    - "deep cross modal hashing"
    - "efficient training of very deep neural networks for supervised hashing"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "optimizing affinity based binary hashing using auxiliary coordinates"

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)