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: "information theoretic measures of influence based on content dynamics"
  Abstract: The fundamental building block of social influence is for one person to elicit a response in another. Researchers measuring a "response" in social media typically depend either on detailed models of human behavior or on platform-specific cues such as re-tweets, hash tags, URLs, or mentions. Most content on social networks is difficult to model because the modes and motivation of human expression are diverse and incompletely understood. We introduce content transfer, an information-theoretic measure with a predictive interpretation that directly quantifies the strength of the effect of one user's content on another's in a model-free way. Estimating this measure is made possible by combining recent advances in non-parametric entropy estimation with increasingly sophisticated tools for content representation. We demonstrate on Twitter data collected for thousands of users that content transfer is able to capture non-trivial, predictive relationships even for pairs of users not linked in the follower or mention graph. We suggest that this measure makes large quantities of previously under-utilized social media content accessible to rigorous statistical causal analysis.

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 "artificial intelligence for low resource communities influence maximization in an uncertain world"):
  with members:
    - "learning in pomdps with monte carlo tree search"
    - "activating the breakfast club modeling influence spread in natural world social networks"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "incremental pruning a simple fast exact method for partially observable markov decision processes"
    - "maximizing social influence in nearly optimal time"
    - "community structure and scale free collections of erdős renyi graphs"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "detecting social influence in event cascades by comparing discriminative rankers"):
  with members:
    - "deepinf social influence prediction with deep learning"
    - "learning hawkes processes from short doubly censored event sequences"
    - "the social dynamics of language change in online networks"
    - "reconstructing parameters of spreading models from partial observations"
    - "node2vec scalable feature learning for networks"
    - "learning granger causality for hawkes processes"
    - "learning influence functions from incomplete observations"
    - "line large scale information network embedding"

Connected through hyperedge HE-4 (induced by source paper "influence maximization for social good use of social networks in low resource communities"):
  with members:
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "maximizing social influence in nearly optimal time"
    - "community structure and scale free collections of erdős renyi graphs"
    - "social contagion an empirical study of information spread on digg and twitter follower graphs"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"
    - "community detection using a measure of global influence"

Connected through hyperedge HE-5 (induced by source paper "adversarial orthogonal regression two non linear regressions for causal inference"):
  with members:
    - "mine mutual information neural estimation"
    - "k nn estimation of directed information"
    - "adam a method for stochastic optimization"
    - "distinguishing cause from effect using observational data methods and benchmarks"
    - "directed information graphs"
    - "information transfer in social media"

Connected through hyperedge HE-6 (induced by source paper "jidt an information theoretic toolkit for studying the dynamics of complex systems"):
  with members:
    - "assessing coupling dynamics from an ensemble of time series"
    - "efficient transfer entropy analysis of non stationary neural time series"
    - "towards a synergy based approach to measuring information modification"
    - "on active information storage in input driven systems"
    - "shared information new insights and problems in decomposing information in complex systems"
    - "nonnegative decomposition of multivariate information"

Connected through hyperedge HE-7 (induced by source paper "latent self exciting point process model for spatial temporal networks"):
  with members:
    - "modeling information propagation with survival theory"
    - "modeling events with cascades of poisson processes"
    - "learning continuous time social network dynamics"
    - "structure and dynamics of information pathways in online media"
    - "information transfer in social media"

Connected through hyperedge HE-8 (induced by source paper "directed information graphs"):
  with members:
    - "justification of logarithmic loss via the benefit of side information"
    - "causal networks semantics and expressiveness"
    - "universal estimation of directed information"
    - "causal conditioning and instantaneous coupling in causality graphs"
    - "the relation between granger causality and directed information theory a review"
    - "computable bounds for rate distortion with feed forward for stationary and ergodic sources"
    - "directed information and pearl s causal calculus"
    - "causal dependence tree approximations of joint distributions for multiple random processes"

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)