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: "the role of social networks in information diffusion"
  Abstract: Online social networking technologies enable individuals to simultaneously share information with any number of peers. Quantifying the causal effect of these technologies on the dissemination of information requires not only identification of who influences whom, but also of whether individuals would still propagate information in the absence of social signals about that information. We examine the role of social networks in online information diffusion with a large-scale field experiment that randomizes exposure to signals about friends' information sharing among 253 million subjects in situ. Those who are exposed are significantly more likely to spread information, and do so sooner than those who are not exposed. We further examine the relative role of strong and weak ties in information propagation. We show that, although stronger ties are individually more influential, it is the more abundant weak ties who are responsible for the propagation of novel information. This suggests that weak ties may play a more dominant role in the dissemination of information online than currently believed.

Connected through hyperedge HE-1 (induced by source paper "deep learning approach on information diffusion in heterogeneous networks"):
  with members:
    - "coupled deep learning for heterogeneous face recognition"
    - "meta path guided embedding for similarity search in large scale heterogeneous information networks"
    - "node2vec scalable feature learning for networks"
    - "deepcas an end to end predictor of information cascades"
    - "information diffusion in social networks in two phases"
    - "revisiting semi supervised learning with graph embeddings"
    - "spreading processes in multilayer networks"
    - "a survey of heterogeneous information network analysis"

Connected through hyperedge HE-2 (induced by source paper "a kernel of truth determining rumor veracity on twitter by diffusion pattern alone"):
  with members:
    - "xfake explainable fake news detector with visualizations"
    - "certifiable robustness and robust training for graph convolutional networks"
    - "fake news a survey of research detection methods and opportunities"
    - "analysis methods in neural language processing a survey"
    - "a motif based approach for identifying controversy"
    - "kernel graph convolutional neural networks"
    - "csi a hybrid deep model for fake news detection"
    - "some like it hoax automated fake news detection in social networks"

Connected through hyperedge HE-3 (induced by source paper "inapproximability and parameterized results for the target set selection problem"):
  with members:
    - "target set selection problem for honeycomb networks"

Connected through hyperedge HE-4 (induced by source paper "computational human dynamics"):
  with members:
    - "joint embedding of structure and features via graph convolutional networks"
    - "location occupation and semantics based socioeconomic status inference on twitter"
    - "the temporal event graph"
    - "link transmission centrality in large scale social networks"
    - "socioeconomic dependencies of linguistic patterns in twitter a multivariate analysis"
    - "graph embedding techniques applications and performance a survey"
    - "correlations and dynamics of consumption patterns in social economic networks"
    - "stream graphs and link streams for the modeling of interactions over time"

Connected through hyperedge HE-5 (induced by source paper "information diffusion in social networks friendship paradox based models and statistical inference"):
  with members:
    - "what do your friends think efficient polling methods for networks using friendship paradox"
    - "centrality friendship paradoxes when our friends are more important than us"
    - "conjoining uncooperative societies facilitates evolution of cooperation"
    - "contagions in social networks effects of monophilic contagion friendship paradox and reactive networks"
    - "which friends are more popular than you contact strength and the friendship paradox in social networks"
    - "influence maximization over markovian graphs a stochastic optimization approach"
    - "engagement dynamics and sensitivity analysis of youtube videos"
    - "the majority illusion in social networks"

Connected through hyperedge HE-6 (induced by source paper "review on graph feature learning and feature extraction techniques for link prediction"):
  with members:
    - "link prediction based on graph neural networks"
    - "dual convolutional neural network for graph of graphs link prediction"
    - "hierarchical graph representation learning with differentiable pooling"
    - "temporally evolving community detection and prediction in content centric networks"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "graph embedding techniques applications and performance a survey"
    - "temporally evolving community detection and prediction in content centric networks"
    - "representation learning on graphs methods and applications"

Connected through hyperedge HE-7 (induced by source paper "deep social collaborative filtering"):
  with members:
    - "deep adversarial social recommendation"
    - "graph neural networks for social recommendation"
    - "attention is all you need"
    - "neural collaborative filtering"
    - "item silk road recommending items from information domains to social users"
    - "irgan a minimax game for unifying generative and discriminative information retrieval models"
    - "node2vec scalable feature learning for networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "are all successful communities alike characterizing and predicting the success of online communities"):
  with members:
    - "network structure efficiency and performance in wikiprojects"
    - "tracing community genealogy how new communities emerge from the old"
    - "caveat emptor computational social science large scale missing data in a widely published reddit corpus"
    - "a warm welcome matters the link between social feedback and weight loss in r loseit"
    - "loyalty in online communities"
    - "social networks under stress"
    - "the effect of social feedback in a reddit weight loss community"
    - "all who wander on the prevalence and characteristics of multi community engagement"

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)