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: "can cascades be predicted"
  Abstract: On many social networking web sites such as Facebook and Twitter, resharing or reposting functionality allows users to share others' content with their own friends or followers. As content is reshared from user to user, large cascades of reshares can form. While a growing body of research has focused on analyzing and characterizing such cascades, a recent, parallel line of work has argued that the future trajectory of a cascade may be inherently unpredictable. In this work, we develop a framework for addressing cascade prediction problems. On a large sample of photo reshare cascades on Facebook, we find strong performance in predicting whether a cascade will continue to grow in the future. We find that the relative growth of a cascade becomes more predictable as we observe more of its reshares, that temporal and structural features are key predictors of cascade size, and that initially, breadth, rather than depth in a cascade is a better indicator of larger cascades. This prediction performance is robust in the sense that multiple distinct classes of features all achieve similar performance. We also discover that temporal features are predictive of a cascade's eventual shape. Observing independent cascades of the same content, we find that while these cascades differ greatly in size, we are still able to predict which ends up the largest.

Connected through hyperedge HE-1 (induced by source paper "deep collaborative embedding for information cascade prediction"):
  with members:
    - "dyngraph2vec capturing network dynamics using dynamic graph representation learning"
    - "iad interaction aware diffusion framework in social networks"
    - "deepinf social influence prediction with deep learning"
    - "attributed social network embedding"
    - "collaborative inference of coexisting information diffusions"
    - "a survey on network embedding"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "topological recurrent neural network for diffusion prediction"

Connected through hyperedge HE-2 (induced by source paper "socialgrid a tcn enhanced method for online discussion forecasting"):
  with members:
    - "nestpp modeling thread dynamics in online discussion forums"
    - "modelling structure and predicting dynamics of discussion threads in online boards"
    - "personalized thread recommendation for mooc discussion forums"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "time series classification from scratch with deep neural networks a strong baseline"
    - "multi scale convolutional neural networks for time series classification"
    - "tideh time dependent hawkes process for predicting retweet dynamics"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-3 (induced by source paper "learning mixtures of graphs from epidemic cascades"):
  with members:
    - "learning graphs from noisy epidemic cascades"
    - "meta learning with relational information for short sequences"
    - "em converges for a mixture of many linear regressions"
    - "the cost of uncertainty in curing epidemics"
    - "fake news mitigation via point process based intervention"
    - "list decodable robust mean estimation and learning mixtures of spherical gaussians"
    - "characterization and inference of graph diffusion processes from observations of stationary signals"
    - "solving a mixture of many random linear equations by tensor decomposition and alternating minimization"

Connected through hyperedge HE-4 (induced by source paper "deepcp deep learning driven cascade prediction based autonomous content placement in closed social network"):
  with members:
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "edge ai on demand accelerating deep neural network inference via edge computing"
    - "improved techniques for training gans"
    - "social network analysis inspired content placement with qos in cloud based content delivery networks"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "adam a method for stochastic optimization"
    - "modeling and predicting popularity dynamics via reinforced poisson processes"
    - "living on the edge the role of proactive caching in 5g wireless networks"

Connected through hyperedge HE-5 (induced by source paper "nestpp modeling thread dynamics in online discussion forums"):
  with members:
    - "modelling structure and predicting dynamics of discussion threads in online boards"
    - "personalized thread recommendation for mooc discussion forums"
    - "expecting to be hip hawkes intensity processes for social media popularity"
    - "tideh time dependent hawkes process for predicting retweet dynamics"
    - "feature driven and point process approaches for popularity prediction"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "from user comments to on line conversations"

Connected through hyperedge HE-6 (induced by source paper "popularity prediction on social platforms with coupled graph neural networks"):
  with members:
    - "graph wavelet neural network"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hierarchical graph representation learning with differentiable pooling"
    - "deepinf social influence prediction with deep learning"
    - "how powerful are graph neural networks"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "expecting to be hip hawkes intensity processes for social media popularity"
    - "neural message passing for quantum chemistry"

Connected through hyperedge HE-7 (induced by source paper "node embedding over temporal graphs"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "dyngem deep embedding method for dynamic graphs"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "graph embedding techniques applications and performance a survey"
    - "representation learning over dynamic graphs"
    - "know evolve deep temporal reasoning for dynamic knowledge graphs"
    - "representation learning on graphs methods and applications"
    - "a survey on network embedding"

Connected through hyperedge HE-8 (induced by source paper "estimating attention flow in online video networks"):
  with members:
    - "offline evaluation of ranking policies with click models"
    - "top k off policy correction for a reinforce recommender system"
    - "sir hawkes linking epidemic models and hawkes processes to model diffusions in finite populations"
    - "foundations of sequence to sequence modeling for time series"
    - "beyond views measuring and predicting engagement on youtube videos"
    - "online popularity under promotion viral potential forecasting and the economics of time"
    - "expecting to be hip hawkes intensity processes for social media popularity"
    - "do cascades recur"

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)