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: "exploring limits to prediction in complex social systems"
  Abstract: How predictable is success in complex social systems? In spite of a recent profusion of prediction studies that exploit online social and information network data, this question remains unanswered, in part because it has not been adequately specified. In this paper we attempt to clarify the question by presenting a simple stylized model of success that attributes prediction error to one of two generic sources: insufficiency of available data and/or models on the one hand; and inherent unpredictability of complex social systems on the other. We then use this model to motivate an illustrative empirical study of information cascade size prediction on Twitter. Despite an unprecedented volume of information about users, content, and past performance, our best performing models can explain less than half of the variance in cascade sizes. In turn, this result suggests that even with unlimited data predictive performance would be bounded well below deterministic accuracy. Finally, we explore this potential bound theoretically using simulations of a diffusion process on a random scale free network similar to Twitter. We show that although higher predictive power is possible in theory, such performance requires a homogeneous system and perfect ex-ante knowledge of it: even a small degree of uncertainty in estimating product quality or slight variation in quality across products leads to substantially more restrictive bounds on predictability. We conclude that realistic bounds on predictive accuracy are not dissimilar from those we have obtained empirically, and that such bounds for other complex social systems for which data is more difficult to obtain are likely even lower.

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 "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"

Connected through hyperedge HE-3 (induced by source paper "smp challenge an overview of social media prediction challenge 2019"):
  with members:
    - "sequential prediction of social media popularity with deep temporal context networks"
    - "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"
    - "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 "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"
    - "feature driven and point process approaches for popularity prediction"
    - "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 "hot streaks on social media"):
  with members:
    - "a long term analysis of polarization on twitter"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "the bursty dynamics of the twitter information network"

Connected through hyperedge HE-6 (induced by source paper "learning representations of social media users"):
  with members:
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "what s ur type contextualized classification of user types in marijuana related communications using compositional multiview embedding"
    - "multi task learning for argumentation mining in low resource settings"
    - "semi supervised user geolocation via graph convolutional networks"
    - "cascade contextual sarcasm detection in online discussion forums"
    - "deep generalized canonical correlation analysis"
    - "identifying beneficial task relations for multi task learning in deep neural networks"
    - "tweet2vec learning tweet embeddings using character level cnn lstm encoder decoder"

Connected through hyperedge HE-7 (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"
    - "feature driven and point process approaches for popularity prediction"
    - "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-8 (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"
    - "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"

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)