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: "feature driven and point process approaches for popularity prediction"
  Abstract: Predicting popularity, or the total volume of information outbreaks, is an important subproblem for understanding collective behavior in networks. Each of the two main types of recent approaches to the problem, feature-driven and generative models, have desired qualities and clear limitations. This paper bridges the gap between these solutions with a new hybrid approach and a new performance benchmark. We model each social cascade with a marked Hawkes self-exciting point process, and estimate the content virality, memory decay, and user influence. We then learn a predictive layer for popularity prediction using a collection of cascade history. To our surprise, Hawkes process with a predictive overlay outperform recent feature-driven and generative approaches on existing tweet data [44] and a new public benchmark on news tweets. We also found that a basic set of user features and event time summary statistics performs competitively in both classification and regression tasks, and that adding point process information to the feature set further improves predictions. From these observations, we argue that future work on popularity prediction should compare across feature-driven and generative modeling approaches in both classification and regression tasks.

Connected through hyperedge HE-1 (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"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "can cascades be predicted"
    - "from user comments to on line conversations"

Connected through hyperedge HE-2 (induced by source paper "long term trends of diversity online"):
  with members:
    - "expecting to be hip hawkes intensity processes for social media popularity"

Connected through hyperedge HE-3 (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-4 (induced by source paper "modeling information cascades with self exciting processes via generalized epidemic models"):
  with members:
    - "arming the public with artificial intelligence to counter social bots"
    - "modeling popularity in asynchronous social media streams with recurrent neural networks"
    - "expecting to be hip hawkes intensity processes for social media popularity"
    - "the neural hawkes process a neurally self modulating multivariate point process"
    - "exploring limits to prediction in complex social systems"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "uncovering the temporal dynamics of diffusion networks"

Connected through hyperedge HE-5 (induced by source paper "patent citation dynamics modeling via multi attention recurrent networks"):
  with members:
    - "attention is all you need"
    - "modeling the intensity function of point process via recurrent neural networks"
    - "effective approaches to attention based neural machine translation"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"
    - "network in network"

Connected through hyperedge HE-6 (induced by source paper "modeling popularity in asynchronous social media streams with recurrent neural networks"):
  with members:
    - "online popularity under promotion viral potential forecasting and the economics of time"
    - "expecting to be hip hawkes intensity processes for social media popularity"
    - "marked temporal dynamics modeling based on recurrent neural network"
    - "modeling the intensity function of point process via recurrent neural networks"
    - "tideh time dependent hawkes process for predicting retweet dynamics"
    - "exploring limits to prediction in complex social systems"
    - "recurrent neural networks for driver activity anticipation via sensory fusion architecture"
    - "seismic a self exciting point process model for predicting tweet popularity"

Connected through hyperedge HE-7 (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"
    - "exploring limits to prediction in complex social systems"
    - "modeling and predicting popularity dynamics of microblogs using self excited hawkes processes"
    - "from micro to macro uncovering and predicting information cascading process with behavioral dynamics"

Connected through hyperedge HE-8 (induced by source paper "will this video go viral explaining and predicting the popularity of youtube videos"):
  with members:
    - "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"
    - "exploring limits to prediction in complex social systems"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "characterizing the life cycle of online news stories using social media reactions"

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)