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: "from micro to macro uncovering and predicting information cascading process with behavioral dynamics"
  Abstract: Cascades are ubiquitous in various network environments. How to predict these cascades is highly nontrivial in several vital applications, such as viral marketing, epidemic prevention and traffic management. Most previous works mainly focus on predicting the final cascade sizes. As cascades are typical dynamic processes, it is always interesting and important to predict the cascade size at any time, or predict the time when a cascade will reach a certain size (e.g. an threshold for outbreak). In this paper, we unify all these tasks into a fundamental problem: cascading process prediction. That is, given the early stage of a cascade, how to predict its cumulative cascade size of any later time? For such a challenging problem, how to understand the micro mechanism that drives and generates the macro phenomenons (i.e. cascading proceese) is essential. Here we introduce behavioral dynamics as the micro mechanism to describe the dynamic process of a node's neighbors get infected by a cascade after this node get infected (i.e. one-hop subcascades). Through data-driven analysis, we find out the common principles and patterns lying in behavioral dynamics and propose a novel Networked Weibull Regression model for behavioral dynamics modeling. After that we propose a novel method for predicting cascading processes by effectively aggregating behavioral dynamics, and propose a scalable solution to approximate the cascading process with a theoretical guarantee. We extensively evaluate the proposed method on a large scale social network dataset. The results demonstrate that the proposed method can significantly outperform other state-of-the-art baselines in multiple tasks including cascade size prediction, outbreak time prediction and cascading process prediction.

Connected through hyperedge HE-1 (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-2 (induced by source paper "sir hawkes linking epidemic models and hawkes processes to model diffusions in finite populations"):
  with members:
    - "expecting to be hip hawkes intensity processes for social media popularity"
    - "the neural hawkes process a neurally self modulating multivariate point process"
    - "steering opinion dynamics in information diffusion networks"
    - "a stochastic differential equation framework for guiding online user activities in closed loop"
    - "tideh time dependent hawkes process for predicting retweet dynamics"
    - "feature driven and point process approaches for popularity prediction"
    - "exploring limits to prediction in complex social systems"
    - "modeling and predicting popularity dynamics of microblogs using self excited hawkes processes"

Connected through hyperedge HE-3 (induced by source paper "joint modeling of event sequence and time series with attentional twin recurrent neural networks"):
  with members:
    - "recurrent poisson factorization for temporal recommendation"
    - "distilling information reliability and source trustworthiness from digital traces"
    - "modeling the intensity function of point process via recurrent neural networks"
    - "multistage campaigning in social networks"
    - "learning granger causality for hawkes processes"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"
    - "recurrent neural networks for driver activity anticipation via sensory fusion architecture"
    - "show attend and tell neural image caption generation with visual attention"

Connected through hyperedge HE-4 (induced by source paper "understanding and forecasting lifecycle events in information cascades"):
  with members:
    - "toward order of magnitude cascade prediction"
    - "understanding the influence of all nodes in a network"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "the bursty dynamics of the twitter information network"
    - "can cascades be predicted"
    - "limited attention and centrality in social networks"
    - "rethinking centrality the role of dynamical processes in social network analysis"

Connected through hyperedge HE-5 (induced by source paper "structural patterns of information cascades and their implications for dynamics and semantics"):
  with members:
    - "do cascades recur"
    - "exploring limits to prediction in complex social systems"
    - "the bursty dynamics of the twitter information network"
    - "can cascades be predicted"

Connected through hyperedge HE-6 (induced by source paper "deepcas an end to end predictor of information cascades"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "learning convolutional neural networks for graphs"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "subgraph2vec learning distributed representations of rooted sub graphs from large graphs"
    - "deepgraph graph structure predicts network growth"
    - "toward order of magnitude cascade prediction"
    - "line large scale information network embedding"
    - "seismic a self exciting point process model for predicting tweet popularity"

Connected through hyperedge HE-7 (induced by source paper "predictability of popularity gaps between prediction and understanding"):
  with members:
    - "distinguishing between personal preferences and social influence in online activity feeds"
    - "studying and modeling the connection between people s preferences and content sharing"
    - "the simple rules of social contagion"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "can cascades be predicted"
    - "virality prediction and community structure in social networks"
    - "the pulse of news in social media forecasting popularity"
    - "the untold story of the clones content agnostic factors that impact youtube video popularity"

Connected through hyperedge HE-8 (induced by source paper "exploring limits to prediction in complex social systems"):
  with members:
    - "predictability of popularity gaps between prediction and understanding"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "can cascades be predicted"
    - "dance hit song prediction"
    - "predicting successful memes using network and community structure"
    - "virality prediction and community structure in social networks"
    - "on the interplay between social and topical structure"
    - "twitter mood predicts the stock market"

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)