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 saliency hashing"
  Abstract: In recent years, hashing methods have been proved efficient for large-scale Web media search. However, existing general hashing methods have limited discriminative power for describing fine-grained objects that share similar overall appearance but have subtle difference. To solve this problem, we for the first time introduce attention mechanism to the learning of hashing codes. Specifically, we propose a novel deep hashing model, named deep saliency hashing (DSaH), which automatically mines salient regions and learns semantic-preserving hashing codes simultaneously. DSaH is a two-step end-to-end model consisting of an attention network and a hashing network. Our loss function contains three basic components, including the semantic loss, the saliency loss, and the quantization loss. The saliency loss guides the attention network to mine discriminative regions from pairs of images. We conduct extensive experiments on both fine-grained and general retrieval datasets for performance evaluation. Experimental results on Oxford Flowers-17 and Stanford Dogs-120 demonstrate that our DSaH performs the best for fine-grained retrieval task and beats the existing best retrieval performance (DPSH) by approximately 12%. DSaH also outperforms several state-of-the-art hashing methods on general datasets, including CIFAR-10 and NUS-WIDE.

Connected through hyperedge HE-1 (induced by source paper "simultaneous region localization and hash coding for fine grained image retrieval"):
  with members:
    - "fast supervised discrete hashing"
    - "deep policy hashing network with listwise supervision"
    - "deep triplet quantization"
    - "feature pyramid hashing"
    - "ssdh semi supervised deep hashing for large scale image retrieval"
    - "multi attention multi class constraint for fine grained image recognition"
    - "deep semantic hashing with generative adversarial networks"
    - "learning to navigate for fine grained classification"

Connected through hyperedge HE-2 (induced by source paper "deep attention guided hashing"):
  with members:
    - "deep priority hashing"
    - "deep supervised discrete hashing"
    - "deep binary reconstruction for cross modal hashing"
    - "hashnet deep learning to hash by continuation"
    - "pose invariant embedding for deep person re identification"
    - "deep binaries encoding semantic rich cues for efficient textual visual cross retrieval"
    - "diversified visual attention networks for fine grained object classification"
    - "fully convolutional attention localization networks efficient attention localization for fine grained recognition"

Connected through hyperedge HE-3 (induced by source paper "feature pyramid hashing"):
  with members:
    - "deep semantic hashing with generative adversarial networks"
    - "deep feature pyramid reconfiguration for object detection"
    - "adversarial learning for fine grained image search"
    - "hashnet deep learning to hash by continuation"
    - "cascaded pyramid network for multi person pose estimation"
    - "fast training of triplet based deep binary embedding networks"
    - "a survey on learning to hash"
    - "feature pyramid networks for object detection"

Connected through hyperedge HE-4 (induced by source paper "ssah semi supervised adversarial deep hashing with self paced hard sample generation"):
  with members:
    - "deep triplet quantization"
    - "towards optimal discrete online hashing with balanced similarity"
    - "ssdh semi supervised deep hashing for large scale image retrieval"
    - "deep semantic hashing with generative adversarial networks"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "ganimation anatomically aware facial animation from a single image"
    - "random erasing data augmentation"
    - "a fast rcnn hard positive generation via adversary for object detection"

Connected through hyperedge HE-5 (induced by source paper "fmhash deep hashing of in air handwriting for user identification"):
  with members:
    - "tensorflow a system for large scale machine learning"
    - "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"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

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)