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: "learning to hash with binary deep neural network"
  Abstract: This work proposes deep network models and learning algorithms for unsupervised and supervised binary hashing. Our novel network design constrains one hidden layer to directly output the binary codes. This addresses a challenging issue in some previous works: optimizing non-smooth objective functions due to binarization. Moreover, we incorporate independence and balance properties in the direct and strict forms in the learning. Furthermore, we include similarity preserving property in our objective function. Our resulting optimization with these binary, independence, and balance constraints is difficult to solve. We propose to attack it with alternating optimization and careful relaxation. Experimental results on three benchmark datasets show that our proposed methods compare favorably with the state of the art.

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 "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"
    - "deep supervised hashing with triplet labels"
    - "identity mappings in deep residual networks"
    - "efficient training of very deep neural networks for supervised hashing"

Connected through hyperedge HE-3 (induced by source paper "deep semantic multimodal hashing network for scalable multimedia retrieval"):
  with members:
    - "deep ordinal hashing with spatial attention"
    - "supervised learning of semantics preserving hash via deep convolutional neural networks"
    - "deep supervised discrete hashing"
    - "part based deep hashing for large scale person re identification"
    - "mask r cnn"
    - "object detection from video tubelets with convolutional neural networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "sca cnn spatial and channel wise attention in convolutional networks for image captioning"

Connected through hyperedge HE-4 (induced by source paper "deep ordinal hashing with spatial attention"):
  with members:
    - "supervised learning of semantics preserving hash via deep convolutional neural networks"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "end to end comparative attention networks for person re identification"
    - "deep region hashing for efficient large scale instance search from images"
    - "supervising neural attention models for video captioning by human gaze data"
    - "fooling vision and language models despite localization and attention mechanism"
    - "image captioning with semantic attention"
    - "weakly supervised cascaded convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "simultaneous feature aggregating and hashing for compact binary code learning"):
  with members:
    - "on device scalable image based localization via prioritized cascade search and fast one many ransac"
    - "deep supervised discrete hashing"
    - "simultaneous feature aggregating and hashing for large scale image search"
    - "embedding based on function approximation for large scale image search"
    - "a survey on learning to hash"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "bilinear random projections for locality sensitive binary codes"

Connected through hyperedge HE-6 (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-7 (induced by source paper "deep triplet quantization"):
  with members:
    - "subic a supervised structured binary code for image search"
    - "hashnet deep learning to hash by continuation"
    - "composite correlation quantization for efficient multimodal retrieval"
    - "a survey on learning to hash"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "feature learning based deep supervised hashing with pairwise labels"
    - "deep residual learning for image recognition"
    - "optimized cartesian k means"

Connected through hyperedge HE-8 (induced by source paper "on device scalable image based localization via prioritized cascade search and fast one many ransac"):
  with members:
    - "geometric loss functions for camera pose regression with deep learning"
    - "image based localization using hourglass networks"
    - "simultaneous feature aggregating and hashing for large scale image search"
    - "selective deep convolutional features for image retrieval"
    - "image based localization using lstms for structured feature correlation"
    - "binary hashing with semidefinite relaxation and augmented lagrangian"
    - "posenet a convolutional network for real time 6 dof camera relocalization"
    - "metric localization using google street view"

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)