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: "pay attention to virality understanding popularity of social media videos with the attention mechanism"
  Abstract: Predicting popularity of social media videos before they are published is a challenging task, mainly due to the complexity of content distribution network as well as the number of factors that play part in this process. As solving this task provides tremendous help for media content creators, many successful methods were proposed to solve this problem with machine learning. In this work, we change the viewpoint and postulate that it is not only the predicted popularity that matters, but also, maybe even more importantly, understanding of how individual parts influence the final popularity score. To that end, we propose to combine the Grad-CAM visualization method with a soft attention mechanism. Our preliminary results show that this approach allows for more intuitive interpretation of the content impact on video popularity, while achieving competitive results in terms of prediction accuracy.

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"
    - "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"
    - "fast matrix factorization for online recommendation with implicit feedback"

Connected through hyperedge HE-2 (induced by source paper "intrinsic image popularity assessment"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "how to become instagram famous post popularity prediction with dual attention"
    - "sequential prediction of social media popularity with deep temporal context networks"
    - "cats and captions vs creators and the clock comparing multimodal content to context in predicting relative popularity"
    - "modeling image virality with pairwise spatial transformer networks"
    - "time matters multi scale temporalization of social media popularity"
    - "dipiq blind image quality assessment by learning to rank discriminable image pairs"
    - "predicting popularity of online videos using support vector regression"

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)