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: "structure and dynamics of information pathways in online media"
  Abstract: Diffusion of information, spread of rumors and infectious diseases are all instances of stochastic processes that occur over the edges of an underlying network. Many times networks over which contagions spread are unobserved, and such networks are often dynamic and change over time. In this paper, we investigate the problem of inferring dynamic networks based on information diffusion data. We assume there is an unobserved dynamic network that changes over time, while we observe the results of a dynamic process spreading over the edges of the network. The task then is to infer the edges and the dynamics of the underlying network. #R##N#We develop an on-line algorithm that relies on stochastic convex optimization to efficiently solve the dynamic network inference problem. We apply our algorithm to information diffusion among 3.3 million mainstream media and blog sites and experiment with more than 179 million different pieces of information spreading over the network in a one year period. We study the evolution of information pathways in the online media space and find interesting insights. Information pathways for general recurrent topics are more stable across time than for on-going news events. Clusters of news media sites and blogs often emerge and vanish in matter of days for on-going news events. Major social movements and events involving civil population, such as the Libyan's civil war or Syria's uprise, lead to an increased amount of information pathways among blogs as well as in the overall increase in the network centrality of blogs and social media sites.

Connected through hyperedge HE-1 (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-2 (induced by source paper "when less is more systematic analysis of cascade based community detection"):
  with members:
    - "systematic analysis of cluster similarity indices towards bias free cluster validation"
    - "community detection through likelihood optimization in search of a sound model"
    - "dani a fast diffusion aware network inference algorithm"
    - "equivalence between modularity optimization and maximum likelihood methods for community detection"
    - "metrics for community analysis a survey"
    - "estimating diffusion network structures recovery conditions sample complexity soft thresholding algorithm"
    - "modeling information propagation with survival theory"
    - "submodular inference of diffusion networks from multiple trees"

Connected through hyperedge HE-3 (induced by source paper "learning multivariate hawkes processes at scale"):
  with members:
    - "predicting dynamic embedding trajectory in temporal interaction networks"
    - "latent odes for irregularly sampled time series"
    - "community interaction and conflict on the web"
    - "sir hawkes linking epidemic models and hawkes processes to model diffusions in finite populations"
    - "fake news mitigation via point process based intervention"
    - "wasserstein learning of deep generative point process models"
    - "learning from clinical judgments semi markov modulated marked hawkes processes for risk prognosis"
    - "learning granger causality for hawkes processes"

Connected through hyperedge HE-4 (induced by source paper "self attentive hawkes processes"):
  with members:
    - "self attention with structural position representations"
    - "intensity free learning of temporal point processes"
    - "fully neural network based model for general temporal point processes"
    - "learning temporal point processes via reinforcement learning"
    - "self attention with relative position representations"
    - "benchmarking deep sequential models on volatility predictions for financial time series"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "non local neural networks"

Connected through hyperedge HE-5 (induced by source paper "learning graphs from noisy epidemic cascades"):
  with members:
    - "the cost of uncertainty in curing epidemics"
    - "fake news mitigation via point process based intervention"
    - "hiding the rumor source"
    - "correlated cascades compete or cooperate"
    - "a lower bound on the performance of dynamic curing policies for epidemics on graphs"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "spy vs spy rumor source obfuscation"
    - "an efficient curing policy for epidemics on graphs"

Connected through hyperedge HE-6 (induced by source paper "neural diffusion model for microscopic cascade prediction"):
  with members:
    - "attention is all you need"
    - "who will share my image predicting the content diffusion path in online social networks"
    - "topological recurrent neural network for diffusion prediction"
    - "semi supervised classification with graph convolutional networks"
    - "deepcas an end to end predictor of information cascades"
    - "deep residual learning for image recognition"
    - "the simple rules of social contagion"
    - "line large scale information network embedding"

Connected through hyperedge HE-7 (induced by source paper "weseer visual analysis for better information cascade prediction of wechat articles"):
  with members:
    - "modeling and predicting popularity dynamics of microblogs using self excited hawkes processes"
    - "the lifecycle and cascade of wechat social messaging groups"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "can cascades be predicted"
    - "popularity prediction in microblogging network a case study on sina weibo"
    - "cumulative effect in information diffusion empirical study on a microblogging network"
    - "the pulse of news in social media forecasting popularity"

Connected through hyperedge HE-8 (induced by source paper "dynamic network model from partial observations"):
  with members:
    - "topological recurrent neural network for diffusion prediction"
    - "deepcas an end to end predictor of information cascades"
    - "submodular inference of diffusion networks from multiple trees"
    - "uncovering the temporal dynamics of diffusion networks"
    - "on the convexity of latent social network inference"
    - "nearly linear time algorithms for preconditioning and solving symmetric diagonally dominant linear systems"

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)