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: "modeling information propagation with survival theory"
  Abstract: Networks provide a skeleton for the spread of contagions, like, information, ideas, behaviors and diseases. Many times networks over which contagions diffuse are unobserved and need to be inferred. Here we apply survival theory to develop general additive and multiplicative risk models under which the network inference problems can be solved efficiently by exploiting their convexity. Our additive risk model generalizes several existing network inference models. We show all these models are particular cases of our more general model. Our multiplicative model allows for modeling scenarios in which a node can either increase or decrease the risk of activation of another node, in contrast with previous approaches, which consider only positive risk increments. We evaluate the performance of our network inference algorithms on large synthetic and real cascade datasets, and show that our models are able to predict the length and duration of cascades in real data.

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 "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"
    - "structure and dynamics of information pathways in online media"
    - "submodular inference of diffusion networks from multiple trees"

Connected through hyperedge HE-3 (induced by source paper "contrastive training for models of information cascades"):
  with members:
    - "estimating diffusion network structures recovery conditions sample complexity soft thresholding algorithm"
    - "trace complexity of network inference"
    - "structure and dynamics of information pathways in online media"
    - "uncovering the temporal dynamics of diffusion networks"
    - "on the convexity of latent social network inference"

Connected through hyperedge HE-4 (induced by source paper "marked temporal dynamics modeling based on recurrent neural network"):
  with members:
    - "orthogonal rnns and long memory tasks"
    - "modeling and predicting popularity dynamics of microblogs using self excited hawkes processes"
    - "adam a method for stochastic optimization"
    - "modeling and predicting popularity dynamics via reinforced poisson processes"
    - "deepwalk online learning of social representations"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "predicting the popularity of online content"

Connected through hyperedge HE-5 (induced by source paper "learning sentimental influences from users behaviors"):
  with members:
    - "opinion maximization in social networks"
    - "learning user specific latent influence and susceptibility from information cascades"
    - "distributed representations of words and phrases and their compositionality"
    - "staticgreedy solving the scalability accuracy dilemma in influence maximization"
    - "adadelta an adaptive learning rate method"
    - "structure and dynamics of information pathways in online media"
    - "how bad is forming your own opinion"
    - "uncovering the temporal dynamics of diffusion networks"

Connected through hyperedge HE-6 (induced by source paper "steering opinion dynamics in information diffusion networks"):
  with members:
    - "action selection in growing state spaces control of network structure growth"
    - "a stochastic differential equation framework for guiding online user activities in closed loop"
    - "node2vec scalable feature learning for networks"
    - "deep coevolutionary network embedding user and item features for recommendation"
    - "coevolve a joint point process model for information diffusion and network co evolution"
    - "learning and forecasting opinion dynamics in social networks"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "shaping social activity by incentivizing users"

Connected through hyperedge HE-7 (induced by source paper "spatio temporal modeling of users check ins in location based social networks"):
  with members:
    - "back to the past source identification in diffusion networks from partially observed cascades"
    - "correlated cascades compete or cooperate"
    - "modeling adoption and usage of competing products"
    - "latent self exciting point process model for spatial temporal networks"

Connected through hyperedge HE-8 (induced by source paper "correlated cascades compete or cooperate"):
  with members:
    - "multistage campaigning in social networks"
    - "learning granger causality for hawkes processes"
    - "back to the past source identification in diffusion networks from partially observed cascades"
    - "the simple rules of social contagion"
    - "coevolve a joint point process model for information diffusion and network co evolution"
    - "from competition to complementarity comparative influence diffusion and maximization"
    - "estimating diffusion network structures recovery conditions sample complexity soft thresholding algorithm"
    - "shaping social activity by incentivizing users"

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)