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: "sequential prediction of social media popularity with deep temporal context networks"
  Abstract: Prediction of popularity has profound impact for social media, since it offers opportunities to reveal individual preference and public attention from evolutionary social systems. Previous research, although achieves promising results, neglects one distinctive characteristic of social data, i.e., sequentiality. For example, the popularity of online content is generated over time with sequential post streams of social media. To investigate the sequential prediction of popularity, we propose a novel prediction framework called Deep Temporal Context Networks (DTCN) by incorporating both temporal context and temporal attention into account. Our DTCN contains three main components, from embedding, learning to predicting. With a joint embedding network, we obtain a unified deep representation of multi-modal user-post data in a common embedding space. Then, based on the embedded data sequence over time, temporal context learning attempts to recurrently learn two adaptive temporal contexts for sequential popularity. Finally, a novel temporal attention is designed to predict new popularity (the popularity of a new user-post pair) with temporal coherence across multiple time-scales. Experiments on our released image dataset with about 600K Flickr photos demonstrate that DTCN outperforms state-of-the-art deep prediction algorithms, with an average of 21.51% relative performance improvement in the popularity prediction (Spearman Ranking Correlation).

Connected through hyperedge HE-1 (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"
    - "pay attention to virality understanding popularity of social media videos with the attention mechanism"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "estimating early fundraising performance of innovations via graph based market environment model"):
  with members:
    - "temporal relational ranking for stock prediction"
    - "context aware visual compatibility prediction"
    - "neural message passing for quantum chemistry"
    - "geometric deep learning going beyond euclidean data"
    - "gated graph sequence neural networks"
    - "contextual lstm clstm models for large scale nlp tasks"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"

Connected through hyperedge HE-3 (induced by source paper "smp challenge an overview of social media prediction challenge 2019"):
  with members:
    - "time matters multi scale temporalization of social media popularity"
    - "tideh time dependent hawkes process for predicting retweet dynamics"
    - "yelp dataset challenge review rating prediction"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "predictability of popularity gaps between prediction and understanding"
    - "exploring limits to prediction in complex social systems"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "online popularity and topical interests through the lens of instagram"

Connected through hyperedge HE-4 (induced by source paper "a cross repository model for predicting popularity in github"):
  with members:
    - "a communication protocol for man machine networks"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "sequence to sequence learning with neural networks"
    - "generating sequences with recurrent neural networks"
    - "information transfer in social media"

Connected through hyperedge HE-5 (induced by source paper "learning from history and present next item recommendation via discriminatively exploiting user behaviors"):
  with members:
    - "neural collaborative filtering"
    - "a survey on network embedding"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "neural attentive session based recommendation"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"
    - "a probabilistic view of neighborhood based recommendation methods"
    - "item2vec neural item embedding for collaborative filtering"

Connected through hyperedge HE-6 (induced by source paper "woman metal white vs man dress shorts combining social temporal and image signals to understand popularity of pinterest fashion boards"):
  with members:
    - "cats and captions vs creators and the clock comparing multimodal content to context in predicting relative popularity"
    - "cultural diffusion and trends in facebook photographs"
    - "evolution of fashion brands on twitter and instagram"
    - "pinterest board recommendation for twitter users"
    - "densecap fully convolutional localization networks for dense captioning"
    - "social bootstrapping how pinterest and last fm social communities benefit by borrowing links from facebook"

Connected through hyperedge HE-7 (induced by source paper "a multimodal approach to predict social media popularity"):
  with members:
    - "time matters multi scale temporalization of social media popularity"
    - "predicting popularity of online videos using support vector regression"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "an iterative refinement approach for social media headline prediction"):
  with members:
    - "time matters multi scale temporalization of social media popularity"
    - "efficient estimation of word representations in vector space"

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)