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: "a latent social approach to youtube popularity prediction"
  Abstract: Current works on Information Centric Networking assume the spectrum of caching strategies under the Least Recently/Frequently Used (LRFU) scheme as the de-facto standard, due to the ease of implementation and easier analysis of such strategies. In this paper we predict the popularity distribution of YouTube videos within a campus network. We explore two broad approaches in predicting the popularity of videos in the network: consensus approaches based on aggregate behavior in the network, and social approaches based on the information diffusion over an implicit network. We measure the performance of our approaches under a simple caching framework by picking the k most popular videos according to our predicted distribution and calculating the hit rate on the cache. We develop our approach by first incorporating video inter-arrival time (based on the power-law distribution governing the transmission time between two receivers of the same message in scale-free networks) to the baseline (LRFU), then combining with an information diffusion model over the inferred latent social graph that governs diffusion of videos in the network. We apply techniques from latent social network inference to learn the sharing probabilities between users in the network and apply a virus propagation model borrowed from mathematical epidemiology to estimate the number of times a video will be accessed in the future. Our approach gives rise to a 14% hit rate improvement over the baseline.

Connected through hyperedge HE-1 (induced by source paper "user aware folk popularity rank user popularity based tag recommendation that can enhance social popularity"):
  with members:
    - "stacked cross attention for image text matching"
    - "a multimodal approach to predict social media popularity"
    - "tagging like humans diverse and distinct image annotation"
    - "pay attention to virality understanding popularity of social media videos with the attention mechanism"
    - "modeling popularity in asynchronous social media streams with recurrent neural networks"
    - "neural collaborative filtering"
    - "use of deep learning in modern recommendation system a summary of recent works"
    - "stack captioning coarse to fine learning for image captioning"

Connected through hyperedge HE-2 (induced by source paper "collaborations on youtube from unsupervised detection to the impact on video and channel popularity"):
  with members:
    - "voxceleb a large scale speaker identification dataset"
    - "ms celeb 1m a dataset and benchmark for large scale face recognition"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-3 (induced by source paper "modeling image virality with pairwise spatial transformer networks"):
  with members:
    - "deep learning the city quantifying urban perception at a global scale"
    - "end to end localization and ranking for relative attributes"
    - "understanding image virality"
    - "vqa visual question answering"
    - "deep relative attributes"
    - "spatial transformer networks"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"

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.SI(Social and Information Networks)