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 triplet quantization"
  Abstract: Deep hashing establishes efficient and effective image retrieval by end-to-end learning of deep representations and hash codes from similarity data. We present a compact coding solution, focusing on deep learning to quantization approach that has shown superior performance over hashing solutions for similarity retrieval. We propose Deep Triplet Quantization (DTQ), a novel approach to learning deep quantization models from the similarity triplets. To enable more effective triplet training, we design a new triplet selection approach, Group Hard, that randomly selects hard triplets in each image group. To generate compact binary codes, we further apply a triplet quantization with weak orthogonality during triplet training. The quantization loss reduces the codebook redundancy and enhances the quantizability of deep representations through back-propagation. Extensive experiments demonstrate that DTQ can generate high-quality and compact binary codes, which yields state-of-the-art image retrieval performance on three benchmark datasets, NUS-WIDE, CIFAR-10, and MS-COCO.

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"
    - "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"
    - "asymmetric deep semantic quantization for image retrieval"

Connected through hyperedge HE-2 (induced by source paper "generalized product quantization network for semi supervised image retrieval"):
  with members:
    - "end to end supervised product quantization for image search and retrieval"
    - "ssdh semi supervised deep hashing for large scale image retrieval"
    - "a closer look at few shot classification"
    - "open set domain adaptation by backpropagation"
    - "subic a supervised structured binary code for image search"
    - "prototypical networks for few shot learning"
    - "understanding deep learning requires rethinking generalization"
    - "scalable image retrieval by sparse product quantization"

Connected through hyperedge HE-3 (induced by source paper "adversarial attack on deep product quantization network for image retrieval"):
  with members:
    - "end to end supervised product quantization for image search and retrieval"
    - "unsupervised adversarial attacks on deep feature based retrieval with gan"
    - "advertorch v0 1 an adversarial robustness toolbox based on pytorch"
    - "adversarial examples for semantic segmentation and object detection"
    - "universal adversarial perturbations"
    - "towards evaluating the robustness of neural networks"
    - "adversarial examples in the physical world"
    - "optimized cartesian k means"

Connected through hyperedge HE-4 (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"
    - "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"
    - "deep saliency hashing"

Connected through hyperedge HE-5 (induced by source paper "accurate visual localization for automotive applications"):
  with members:
    - "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"
    - "end to end learning of deep visual representations for image retrieval"

Connected through hyperedge HE-6 (induced by source paper "ssah semi supervised adversarial deep hashing with self paced hard sample generation"):
  with members:
    - "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"
    - "deep saliency hashing"
    - "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-7 (induced by source paper "deep spherical quantization for image search"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "triplet center loss for multi view 3d object retrieval"
    - "subic a supervised structured binary code for image search"
    - "deep supervised discrete hashing"
    - "l2 constrained softmax loss for discriminative face verification"
    - "hashnet deep learning to hash by continuation"
    - "a survey on learning to hash"
    - "how should we evaluate supervised hashing"

Connected through hyperedge HE-8 (induced by source paper "deep recurrent quantization for generating sequential binary codes"):
  with members:
    - "deep supervised discrete hashing"
    - "deep supervised hashing with triplet labels"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "feature learning based deep supervised hashing with pairwise labels"
    - "stacked quantizers for compositional vector compression"
    - "fast supervised hashing with decision trees for high dimensional data"

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)