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: "a multilayer approach to multiplexity and link prediction in online geo social networks"
  Abstract: Online social systems are multiplex in nature as multiple links may exist between the same two users across different social networks. In this work, we introduce a framework for studying links and interactions between users beyond the individual social network. Exploring the cross-section of two popular online platforms - Twitter and location-based social network Foursquare - we represent the two together as a composite multilayer online social network. Through this paradigm we study the interactions of pairs of users differentiating between those with links on one or both networks. We find that users with multiplex links, who are connected on both networks, interact more and have greater neighbourhood overlap on both platforms, in comparison with pairs who are connected on just one of the social networks. In particular, the most frequented locations of users are considerably closer, and similarity is considerably greater among multiplex links. We present a number of structural and interaction features, such as the multilayer Adamic/Adar coefficient, which are based on the extension of the concept of the node neighbourhood beyond the single network. Our evaluation, which aims to shed light on the implications of multiplexity for the link generation process, shows that multilayer features, constructed from properties across social networks, perform better than their single network counterparts in predicting links across networks. We propose that combining information from multiple networks in a multilayer configuration can provide new insights into user interactions on online social networks, and can significantly improve link prediction overall with valuable applications to social bootstrapping and friend recommendations.

Connected through hyperedge HE-1 (induced by source paper "simulation and augmentation of social networks for building deep learning models"):
  with members:
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "social networks through the prism of cognition"
    - "graph embedding techniques applications and performance a survey"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "distributed representations of words and phrases and their compositionality"
    - "link prediction by de anonymization how we won the kaggle social network challenge"

Connected through hyperedge HE-2 (induced by source paper "link prediction in multiplex networks based on interlayer similarity"):
  with members:
    - "qanet tensor decomposition approach for query based anomaly detection in heterogeneous information networks"
    - "spreading processes in multilayer networks"
    - "a probabilistic model to resolve diversity accuracy challenge of recommendation systems"
    - "multiple routes transmitted epidemics on multiplex networks"
    - "diffusion of innovations over multiplex social networks"
    - "combinatorial analysis of multiple networks"

Connected through hyperedge HE-3 (induced by source paper "do you trade with your friends or become friends with your trading partners a case study in the g1 cryptocurrency"):
  with members:
    - "weighted bipartite or directed stream graphs for the modeling of temporal networks"
    - "tracking bitcoin users activity using community detection on a network of weak signals"
    - "stream graphs and link streams for the modeling of interactions over time"

Connected through hyperedge HE-4 (induced by source paper "twittermancer predicting interactions on twitter accurately"):
  with members:
    - "twawler a lightweight twitter crawler"
    - "predicting positive and negative links with noisy queries theory practice"
    - "twitter mood predicts the stock market"

Connected through hyperedge HE-5 (induced by source paper "investigating negative interactions in multiplex networks a mutual information approach"):
  with members:
    - "leveraging network dynamics for improved link prediction"
    - "signed link analysis in social media networks"
    - "link prediction in social networks the state of the art"
    - "link prediction in complex networks a mutual information perspective"
    - "applying weighted pagerank to author citation networks"
    - "the slashdot zoo mining a social network with negative edges"

Connected through hyperedge HE-6 (induced by source paper "leveraging friendship networks for dynamic link prediction in social interaction networks"):
  with members:
    - "evaluating link prediction accuracy on dynamic networks with added and removed edges"
    - "dynamic stochastic blockmodels for time evolving social networks"
    - "detecting friendship within dynamic online interaction networks"

Connected through hyperedge HE-7 (induced by source paper "data driven generation of spatio temporal routines in human mobility"):
  with members:
    - "the death and life of great italian cities a mobile phone data perspective"
    - "an analytical framework to nowcast well being using mobile phone data"
    - "a place focused model for social networks in cities"

Connected through hyperedge HE-8 (induced by source paper "sensing and modeling human behavior using social media and mobile data"):
  with members:
    - "anticipatory mobile computing a survey of the state of the art and research challenges"
    - "investigating causality in human behavior from smartphone sensor data a quasi experimental approach"
    - "keep your friends close and your facebook friends closer a multiplex network approach to the analysis of offline and online social ties"
    - "you are what you eat and drink identifying cultural boundaries by analyzing food drink habits in foursquare"
    - "hoodsquare modeling and recommending neighborhoods in location based social networks"
    - "predicting influential users in online 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)