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: "learning and forecasting opinion dynamics in social networks"
  Abstract: Social media and social networking sites have become a global pinboard for exposition and discussion of news, topics, and ideas, where social media users often update their opinions about a particular topic by learning from the opinions shared by their friends. In this context, can we learn a data-driven model of opinion dynamics that is able to accurately forecast opinions from users? In this paper, we introduce SLANT, a probabilistic modeling framework of opinion dynamics, which represents users opinions over time by means of marked jump diffusion stochastic differential equations, and allows for efficient model simulation and parameter estimation from historical fine grained event data. We then leverage our framework to derive a set of efficient predictive formulas for opinion forecasting and identify conditions under which opinions converge to a steady state. Experiments on data gathered from Twitter show that our model provides a good fit to the data and our formulas achieve more accurate forecasting than alternatives.

Connected through hyperedge HE-1 (induced by source paper "consequential ranking algorithms and long term welfare"):
  with members:
    - "policy learning for fairness in ranking"
    - "delayed impact of fair machine learning"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "a short term intervention for long term fairness in the labor market"
    - "equity of attention amortizing individual fairness in rankings"
    - "from fair decision making to social equality"
    - "fairness of exposure in rankings"
    - "deconvolving feedback loops in recommender systems"

Connected through hyperedge HE-2 (induced by source paper "on the complexity of opinions and online discussions"):
  with members:
    - "deep sentence embedding using long short term memory networks analysis and application to information retrieval"
    - "the complexity of computing the minimum rank of a sign pattern matrix"
    - "distributed representations of sentences and documents"
    - "controversy and sentiment in online news"
    - "convolutional neural networks for sentence classification"
    - "an approximation algorithm for approximation rank"

Connected through hyperedge HE-3 (induced by source paper "opinion dynamics with backfire effect and biased assimilation"):
  with members:
    - "opinion maximization in social networks"
    - "selection and influence in cultural dynamics"
    - "biased assimilation homophily and the dynamics of polarization"

Connected through hyperedge HE-4 (induced by source paper "want to bring a community together create more sub communities"):
  with members:
    - "an army of me sockpuppets in online discussion communities"
    - "multistage campaigning in social networks"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "community detection in networks with node attributes"
    - "efficient and principled method for detecting communities in networks"

Connected through hyperedge HE-5 (induced by source paper "distilling information reliability and source trustworthiness from digital traces"):
  with members:
    - "uncovering the dynamics of crowdlearning and the value of knowledge"
    - "multistage campaigning in social networks"
    - "assessing human error against a benchmark of perfection"
    - "smart broadcasting do you want to be seen"
    - "from data fusion to knowledge fusion"
    - "coevolve a joint point process model for information diffusion and network co evolution"
    - "knowledge based trust estimating the trustworthiness of web sources"
    - "estimating diffusion network structures recovery conditions sample complexity soft thresholding algorithm"

Connected through hyperedge HE-6 (induced by source paper "expecting to be hip hawkes intensity processes for social media popularity"):
  with members:
    - "tideh time dependent hawkes process for predicting retweet dynamics"
    - "feature driven and point process approaches for popularity prediction"
    - "do cascades recur"
    - "exploring limits to prediction in complex social systems"
    - "modeling and predicting popularity dynamics of microblogs using self excited hawkes processes"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "shaping social activity by incentivizing users"
    - "modeling and predicting popularity dynamics via reinforced poisson processes"

Connected through hyperedge HE-7 (induced by source paper "a distance measure for the analysis of polar opinion dynamics in social networks"):
  with members:
    - "a tutorial on modeling and analysis of dynamic social networks part i"
    - "diffusion and superposition distances for signals supported on networks"
    - "the bursty dynamics of the twitter information network"
    - "graph based anomaly detection and description a survey"
    - "deltacon a principled massive graph similarity function"
    - "netsimile a scalable approach to size independent network similarity"
    - "user level sentiment analysis incorporating social networks"
    - "uncovering social network sybils in the wild"

Connected through hyperedge HE-8 (induced by source paper "variational policy for guiding point processes"):
  with members:
    - "steering opinion dynamics in information diffusion networks"
    - "fast and simple optimization for poisson likelihood models"
    - "smart broadcasting do you want to be seen"
    - "deep coevolutionary network embedding user and item features for recommendation"
    - "coevolve a joint point process model for information diffusion and network co evolution"
    - "shaping social activity by incentivizing users"
    - "path integral policy improvement with covariance matrix adaptation"

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)