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: "fast training of triplet based deep binary embedding networks"
  Abstract: In this paper, we aim to learn a mapping (or embedding) from images to a compact binary space in which Hamming distances correspond to a ranking measure for the image retrieval task. #R##N#We make use of a triplet loss because this has been shown to be most effective for ranking problems. #R##N#However, training in previous works can be prohibitively expensive due to the fact that optimization is directly performed on the triplet space, where the number of possible triplets for training is cubic in the number of training examples. #R##N#To address this issue, we propose to formulate high-order binary codes learning as a multi-label classification problem by explicitly separating learning into two interleaved stages. #R##N#To solve the first stage, we design a large-scale high-order binary codes inference algorithm to reduce the high-order objective to a standard binary quadratic problem such that graph cuts can be used to efficiently infer the binary code which serve as the label of each training datum. #R##N#In the second stage we propose to map the original image to compact binary codes via carefully designed deep convolutional neural networks (CNNs) and the hashing function fitting can be solved by training binary CNN classifiers. #R##N#An incremental/interleaved optimization strategy is proffered to ensure that these two steps are interactive with each other during training for better accuracy. #R##N#We conduct experiments on several benchmark datasets, which demonstrate both improved training time (by as much as two orders of magnitude) as well as producing state-of-the-art hashing for various retrieval tasks.

Connected through hyperedge HE-1 (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"
    - "deep supervised hashing with triplet labels"
    - "deep cross modal hashing"
    - "a riemannian network for spd matrix learning"
    - "umdfaces an annotated face dataset for training deep networks"

Connected through hyperedge HE-2 (induced by source paper "estimating and abstracting the 3d structure of bones using neural networks on x ray 2d images"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "triplet center loss for multi view 3d object retrieval"
    - "in defense of the triplet loss for person re identification"
    - "cnn architectures for large scale audio classification"
    - "learning local image descriptors with deep siamese and triplet convolutional networks by minimising global loss functions"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "triplet permutation method for deep learning of single shot person re identification"):
  with members:
    - "end to end comparative attention networks for person re identification"
    - "person re identification past present and future"
    - "deep feature learning with relative distance comparison for person re identification"
    - "facenet a unified embedding for face recognition and clustering"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "embarrassingly simple binary representation learning"):
  with members:
    - "zero shot sketch image hashing"
    - "bingan learning compact binary descriptors with a regularized gan"
    - "subic a supervised structured binary code for image search"
    - "mihash online hashing with mutual information"
    - "binary generative adversarial networks for image retrieval"
    - "deep sketch hashing fast free hand sketch based image retrieval"
    - "stochastic generative hashing"
    - "hashnet deep learning to hash by continuation"

Connected through hyperedge HE-5 (induced by source paper "feature pyramid hashing"):
  with members:
    - "deep semantic hashing with generative adversarial networks"
    - "deep feature pyramid reconfiguration for object detection"
    - "deep saliency hashing"
    - "adversarial learning for fine grained image search"
    - "hashnet deep learning to hash by continuation"
    - "cascaded pyramid network for multi person pose estimation"
    - "a survey on learning to hash"
    - "feature pyramid networks for object detection"

Connected through hyperedge HE-6 (induced by source paper "deep policy hashing network with listwise supervision"):
  with members:
    - "fast supervised discrete hashing"
    - "supervised learning of semantics preserving hash via deep convolutional neural networks"
    - "deep reinforcement learning for image hashing"
    - "blockdrop dynamic inference paths in residual networks"
    - "hashnet deep learning to hash by continuation"
    - "a survey on learning to hash"
    - "asynchronous methods for deep reinforcement learning"
    - "simultaneous feature learning and hash coding with deep neural networks"

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"
    - "deep supervised hashing with triplet labels"
    - "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 "joint cluster unary loss for efficient cross modal hashing"):
  with members:
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "semantic cluster unary loss for efficient deep hashing"
    - "self supervised adversarial hashing networks for cross modal retrieval"
    - "deep sketch hashing fast free hand sketch based image retrieval"
    - "correlation hashing network for efficient cross modal retrieval"
    - "deep cross modal hashing"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "feature learning based deep supervised hashing with pairwise labels"

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)