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: "semantic sparse recoding of visual content for image applications"
  Abstract: This paper presents a new semantic sparse recoding method to generate more descriptive and robust representation of visual content for image applications. Although the visual bag-of-words (BOW) representation has been reported to achieve promising results in different image applications, its visual codebook is completely learnt from low-level visual features using quantization techniques and thus the so-called semantic gap remains unbridgeable. To handle such challenging issue, we utilize the annotations (predicted by algorithms or shared by users) of all the images to improve the original visual BOW representation. This is further formulated as a sparse coding problem so that the noise issue induced by the inaccurate quantization of visual features can also be handled to some extent. By developing an efficient sparse coding algorithm, we successfully generate a new visual BOW representation for image applications. Since such sparse coding has actually incorporated the high-level semantic information into the original visual codebook, we thus consider it as semantic sparse recoding of the visual content. Finally, we apply our semantic sparse recoding method to automatic image annotation and social image classification. The experimental results on several benchmark datasets show the promising performance of our semantic sparse recoding method in these two image applications.

Connected through hyperedge HE-1 (induced by source paper "multi modal multi scale deep learning for large scale image annotation"):
  with members:
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "learning from weak and noisy labels for semantic segmentation"
    - "tweet2vec character based distributed representations for social media"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "annotation order matters recurrent image annotator for arbitrary length image tagging"
    - "semantic regularisation for recurrent image annotation"
    - "cnn rnn a unified framework for multi label image classification"

Connected through hyperedge HE-2 (induced by source paper "regularized maximum correntropy machine"):
  with members:
    - "a characterization of cybersecurity posture from network telescope data"
    - "document clustering based on max correntropy non negative matrix factorization"
    - "graph regularized non negative matrix factorization by maximizing correntropy"
    - "maximum mutual information regularized classification"
    - "an evasion and counter evasion study in malicious websites detection"
    - "adaptive learning of region based plsa model for total scene annotation"
    - "latent semantic learning with structured sparse representation for human action recognition"
    - "multiple graph regularized protein domain ranking"

Connected through hyperedge HE-3 (induced by source paper "pairwise constraint propagation on multi view data"):
  with members:
    - "smoothing proximal gradient method for general structured sparse learning"

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)