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: "social influence in social advertising evidence from field experiments"
  Abstract: Social advertising uses information about consumers' peers, including peer affiliations with a brand, product, organization, etc., to target ads and contextualize their display. This approach can increase ad efficacy for two main reasons: peers' affiliations reflect unobserved consumer characteristics, which are correlated along the social network; and the inclusion of social cues (i.e., peers' association with a brand) alongside ads affect responses via social influence processes. For these reasons, responses may be increased when multiple social signals are presented with ads, and when ads are affiliated with peers who are strong, rather than weak, ties.   We conduct two very large field experiments that identify the effect of social cues on consumer responses to ads, measured in terms of ad clicks and the formation of connections with the advertised entity. In the first experiment, we randomize the number of social cues present in word-of-mouth advertising, and measure how responses increase as a function of the number of cues. The second experiment examines the effect of augmenting traditional ad units with a minimal social cue (i.e., displaying a peer's affiliation below an ad in light grey text). On average, this cue causes significant increases in ad performance. Using a measurement of tie strength based on the total amount of communication between subjects and their peers, we show that these influence effects are greatest for strong ties. Our work has implications for ad optimization, user interface design, and central questions in social science research.

Connected through hyperedge HE-1 (induced by source paper "locec local community based edge classification in large online social networks"):
  with members:
    - "a comprehensive survey on graph neural networks"
    - "model slicing for supporting complex analytics with elastic inference cost and resource constraints"
    - "real time influence maximization on dynamic social streams"
    - "revenue maximization in incentivized social advertising"
    - "xgboost a scalable tree boosting system"
    - "database meets deep learning challenges and opportunities"
    - "fast r cnn"
    - "romantic partnerships and the dispersion of social ties a network analysis of relationship status on facebook"

Connected through hyperedge HE-2 (induced by source paper "the opacity problem in social contagion"):
  with members:
    - "information is not a virus and other consequences of human cognitive limits"
    - "node2vec scalable feature learning for networks"
    - "can cascades be predicted"
    - "geotagging one hundred million twitter accounts with total variation minimization"
    - "distributed representations of words and phrases and their compositionality"
    - "virality prediction and community structure in social networks"
    - "what stops social epidemics"

Connected through hyperedge HE-3 (induced by source paper "an experimental study of structural diversity in social networks"):
  with members:
    - "the lifecycles of apps in a social ecosystem"
    - "popularity prediction in microblogging network a case study on sina weibo"
    - "characterizing and curating conversation threads expansion focus volume re entry"
    - "virality prediction and community structure in social networks"
    - "on the interplay between social and topical structure"

Connected through hyperedge HE-4 (induced by source paper "deepinf social influence prediction with deep learning"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "inductive representation learning on large graphs"
    - "attention is all you need"
    - "a century of science globalization of scientific collaborations citations and innovations"
    - "representation learning on graphs methods and applications"
    - "instance normalization the missing ingredient for fast stylization"
    - "structural diversity and homophily a study across more than one hundred big networks"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-5 (induced by source paper "randomized experiments to detect and estimate social influence in networks"):
  with members:
    - "discussion quality diffuses in the digital public square"
    - "designing and deploying online field experiments"
    - "graph cluster randomization network exposure to multiple universes"
    - "selection effects in online sharing consequences for peer adoption"
    - "the role of social networks in information diffusion"
    - "cooperation and contagion in web based networked public goods experiments"
    - "supervised random walks predicting and recommending links in social networks"

Connected through hyperedge HE-6 (induced by source paper "complex contagions a decade in review"):
  with members:
    - "bots as virtual confederates design and ethics"
    - "multi stage complex contagions"
    - "virality prediction and community structure in social networks"
    - "the role of social networks in information diffusion"
    - "modeling the adoption of innovations in the presence of geographic and media influences"
    - "are friends overrated a study for the social news aggregator digg com"

Connected through hyperedge HE-7 (induced by source paper "discussion quality diffuses in the digital public square"):
  with members:
    - "tweet2vec learning tweet embeddings using character level cnn lstm encoder decoder"
    - "winning arguments interaction dynamics and persuasion strategies in good faith online discussions"
    - "distributed representations of sentences and documents"
    - "word2vec explained deriving mikolov et al s negative sampling word embedding method"
    - "how community feedback shapes user behavior"
    - "from user comments to on line conversations"

Connected through hyperedge HE-8 (induced by source paper "revenue maximization in incentivized social advertising"):
  with members:
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "how to influence people with partial incentives"
    - "viral marketing meets social advertising ad allocation with minimum regret"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "maximizing social influence in nearly optimal time"
    - "on budgeted influence maximization in social 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)