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: "simultaneous feature learning and hash coding with deep neural networks"
  Abstract: Similarity-preserving hashing is a widely-used method for nearest neighbour search in large-scale image retrieval tasks. For most existing hashing methods, an image is first encoded as a vector of hand-engineering visual features, followed by another separate projection or quantization step that generates binary codes. However, such visual feature vectors may not be optimally compatible with the coding process, thus producing sub-optimal hashing codes. In this paper, we propose a deep architecture for supervised hashing, in which images are mapped into binary codes via carefully designed deep neural networks. The pipeline of the proposed deep architecture consists of three building blocks: 1) a sub-network with a stack of convolution layers to produce the effective intermediate image features; 2) a divide-and-encode module to divide the intermediate image features into multiple branches, each encoded into one hash bit; and 3) a triplet ranking loss designed to characterize that one image is more similar to the second image than to the third one. Extensive evaluations on several benchmark image datasets show that the proposed simultaneous feature learning and hash coding pipeline brings substantial improvements over other state-of-the-art supervised or unsupervised 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 "learning to hash with graph neural networks for recommender systems"):
  with members:
    - "is a single vector enough exploring node polysemy for network embedding"
    - "deep representation learning for social network analysis"
    - "graph neural networks for social recommendation"
    - "neural graph collaborative filtering"
    - "deep learning based recommender system a survey and new perspectives"
    - "graph convolutional neural networks for web scale recommender systems"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "hierarchical graph representation learning with differentiable pooling"

Connected through hyperedge HE-3 (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 supervised hashing with triplet labels"
    - "deep cross modal hashing"
    - "a riemannian network for spd matrix learning"

Connected through hyperedge HE-4 (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"
    - "learning to hash with binary deep neural network"

Connected through hyperedge HE-5 (induced by source paper "auto encoding twin bottleneck hashing"):
  with members:
    - "distillhash unsupervised deep hashing by distilling data pairs"
    - "embarrassingly simple binary representation learning"
    - "zero shot sketch image hashing"
    - "bingan learning compact binary descriptors with a regularized gan"
    - "stochastic generative hashing"
    - "hashnet deep learning to hash by continuation"
    - "semi supervised classification with graph convolutional networks"
    - "hashing with binary autoencoders"

Connected through hyperedge HE-6 (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"
    - "deep triplet quantization"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "deep multi view enhancement hashing for image retrieval"):
  with members:
    - "deep multi view learning using neuron wise correlation maximizing regularizers"
    - "hashnet deep learning to hash by continuation"
    - "a survey on learning to hash"
    - "deep cross modal hashing"
    - "using complex networks towards information retrieval and diagnostics in multidimensional imaging"
    - "hashing on nonlinear manifolds"

Connected through hyperedge HE-8 (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"

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)