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: "supervised deep hashing for hierarchical labeled data"
  Abstract: Recently, hashing methods have been widely used in large-scale image retrieval. However, most existing hashing methods did not consider the hierarchical relation of labels, which means that they ignored the rich information stored in the hierarchy. Moreover, most of previous works treat each bit in a hash code equally, which does not meet the scenario of hierarchical labeled data. In this paper, we propose a novel deep hashing method, called supervised hierarchical deep hashing (SHDH), to perform hash code learning for hierarchical labeled data. Specifically, we define a novel similarity formula for hierarchical labeled data by weighting each layer, and design a deep convolutional neural network to obtain a hash code for each data point. Extensive experiments on several real-world public datasets show that the proposed method outperforms the state-of-the-art baselines in the image retrieval task.

Connected through hyperedge HE-1 (induced by source paper "deep cross modal hashing with hashing functions and unified hash codes jointly learning"):
  with members:
    - "collaborative quantization for cross modal similarity search"
    - "deep semantic hashing with generative adversarial networks"
    - "self supervised adversarial hashing networks for cross modal retrieval"
    - "unsupervised triplet hashing for fast image retrieval"
    - "discrete latent factor model for cross modal hashing"
    - "asymmetric deep supervised hashing"
    - "hashnet deep learning to hash by continuation"
    - "deep binaries encoding semantic rich cues for efficient textual visual cross retrieval"

Connected through hyperedge HE-2 (induced by source paper "semantic hierarchy preserving deep hashing for large scale image retrieval"):
  with members:
    - "supervised learning of semantics preserving hash via deep convolutional neural networks"
    - "deep class wise hashing semantics preserving hashing via class wise loss"
    - "deep supervised discrete hashing"
    - "hashnet deep learning to hash by continuation"
    - "deep supervised hashing with triplet labels"
    - "feature learning based deep supervised hashing with pairwise labels"
    - "deep residual learning for image recognition"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"

Connected through hyperedge HE-3 (induced by source paper "shrewd semantic hierarchy based relational embeddings for weakly supervised deep hashing"):
  with members:
    - "semantic hierarchy preserving deep hashing for large scale image retrieval"
    - "hierarchy based image embeddings for semantic image retrieval"
    - "dual asymmetric deep hashing learning"
    - "mean local group average precision mlgap a new performance metric for hashing based retrieval"
    - "label embedding network learning label representation for soft training of deep networks"
    - "unsupervised semantic deep hashing"
    - "hashnet deep learning to hash by continuation"
    - "a survey on learning to hash"

Connected through hyperedge HE-4 (induced by source paper "understanding the impact of label granularity on cnn based image classification"):
  with members:
    - "identity mappings in deep residual networks"
    - "wide residual networks"
    - "weakly supervised deep detection networks"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "deep class wise hashing semantics preserving hashing via class wise loss"):
  with members:
    - "supervised learning of semantics preserving hash via deep convolutional neural networks"
    - "learning deep features via congenerous cosine loss for person recognition"
    - "deep supervised discrete hashing"
    - "hashnet deep learning to hash by continuation"
    - "fast training of triplet based deep binary embedding networks"
    - "deep supervised hashing with triplet labels"
    - "a survey on learning to hash"
    - "fine grained categorization and dataset bootstrapping using deep metric learning with humans in the loop"

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)