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: "when to post on social networks"
  Abstract: For many users on social networks, one of the goals when broadcasting content is to reach a large audience. The probability of receiving reactions to a message differs for each user and depends on various factors, such as location, daily and weekly behavior patterns and the visibility of the message. While previous work has focused on overall network dynamics and message flow cascades, the problem of recommending personalized posting times has remained an under-explored topic of research.   In this study, we formulate a when-to-post problem, where the objective is to find the best times for a user to post on social networks in order to maximize the probability of audience responses. To understand the complexity of the problem, we examine user behavior in terms of post-to-reaction times, and compare cross-network and cross-city weekly reaction behavior for users in different cities, on both Twitter and Facebook. We perform this analysis on over a billion posted messages and observed reactions, and propose multiple approaches for generating personalized posting schedules. We empirically assess these schedules on a sampled user set of 0.5 million active users and more than 25 million messages observed over a 56 day period. We show that users see a reaction gain of up to 17% on Facebook and 4% on Twitter when the recommended posting times are used.   We open the dataset used in this study, which includes timestamps for over 144 million posts and over 1.1 billion reactions. The personalized schedules derived here are used in a fully deployed production system to recommend posting times for millions of users every day.

Connected through hyperedge HE-1 (induced by source paper "midmod osn a microscopic level information diffusion model for online social networks"):
  with members:
    - "predicting online extremism content adopters and interaction reciprocity"
    - "klout score measuring influence across multiple social networks"
    - "predicting the temporal dynamics of information diffusion in social networks"
    - "diffusive logistic model towards predicting information diffusion in online social networks"

Connected through hyperedge HE-2 (induced by source paper "toward maximizing the visibility of content in social media brand pages a temporal analysis"):
  with members:
    - "online popularity under promotion viral potential forecasting and the economics of time"
    - "multistage campaigning in social networks"
    - "klout score measuring influence across multiple social networks"
    - "dynamical classes of collective attention in twitter"
    - "trends in social media persistence and decay"
    - "information contagion an empirical study of the spread of news on digg and twitter social networks"
    - "predicting the popularity of online content"

Connected through hyperedge HE-3 (induced by source paper "can a user guess what her followers want"):
  with members:
    - "steering social activity a stochastic optimal control point of view"
    - "smart broadcasting do you want to be seen"
    - "learning economic parameters from revealed preferences"
    - "how community feedback shapes user behavior"
    - "further optimal regret bounds for thompson sampling"
    - "efficiently learning from revealed preference"
    - "thompson sampling for contextual bandits with linear payoffs"

Connected through hyperedge HE-4 (induced by source paper "can a user anticipate what her followers want"):
  with members:
    - "steering social activity a stochastic optimal control point of view"
    - "learning economic parameters from revealed preferences"
    - "how community feedback shapes user behavior"
    - "further optimal regret bounds for thompson sampling"
    - "efficiently learning from revealed preference"
    - "thompson sampling for contextual bandits with linear payoffs"

Connected through hyperedge HE-5 (induced by source paper "non submodular function maximization subject to a matroid constraint with applications"):
  with members:
    - "submodular maximization beyond non negativity guarantees fast algorithms and applications"
    - "steering social activity a stochastic optimal control point of view"
    - "equity of attention amortizing individual fairness in rankings"
    - "deep reinforcement learning of marked temporal point processes"
    - "fairness of exposure in rankings"
    - "combinatorial penalties which structures are preserved by convex relaxations"
    - "weakly submodular maximization beyond cardinality constraints does randomization help greedy"
    - "gradient methods for submodular maximization"

Connected through hyperedge HE-6 (induced by source paper "online popularity under promotion viral potential forecasting and the economics of time"):
  with members:
    - "expecting to be hip hawkes intensity processes for social media popularity"
    - "smart broadcasting do you want to be seen"
    - "who watches and shares what on youtube and when using twitter to understand youtube viewership"

Connected through hyperedge HE-7 (induced by source paper "smart broadcasting do you want to be seen"):
  with members:
    - "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"
    - "modeling adoption and usage of competing products"
    - "can cascades be predicted"
    - "the effect of wording on message propagation topic and author controlled natural experiments on twitter"

Connected through hyperedge HE-8 (induced by source paper "time centrality in dynamic complex networks"):
  with members:
    - "on multiaspect graphs"
    - "multiaspect graphs algebraic representation and algorithms"
    - "a unifying model for representing time varying graphs"
    - "time varying graphs and dynamic networks"

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)