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: "romantic partnerships and the dispersion of social ties a network analysis of relationship status on facebook"
  Abstract: A crucial task in the analysis of on-line social-networking systems is to identify important people --- those linked by strong social ties --- within an individual's network neighborhood. Here we investigate this question for a particular category of strong ties, those involving spouses or romantic partners. We organize our analysis around a basic question: given all the connections among a person's friends, can you recognize his or her romantic partner from the network structure alone? Using data from a large sample of Facebook users, we find that this task can be accomplished with high accuracy, but doing so requires the development of a new measure of tie strength that we term `dispersion' --- the extent to which two people's mutual friends are not themselves well-connected. The results offer methods for identifying types of structurally significant people in on-line applications, and suggest a potential expansion of existing theories of tie strength.

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"
    - "social influence in social advertising evidence from field experiments"

Connected through hyperedge HE-2 (induced by source paper "i know you ll be back interpretable new user clustering and churn prediction on a mobile social application"):
  with members:
    - "parallel long short term memory for multi stream classification"
    - "cone community oriented network embedding"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "improved semantic representations from tree structured long short term memory networks"
    - "line large scale information network embedding"

Connected through hyperedge HE-3 (induced by source paper "inferring the strength of social ties a community driven approach"):
  with members:
    - "on facebook most ties are weak"

Connected through hyperedge HE-4 (induced by source paper "parameterized aspects of strong subgraph closure"):
  with members:
    - "on the relation of strong triadic closure and cluster deletion"
    - "maximizing the strong triadic closure in split graphs and proper interval graphs"
    - "kernelization lower bounds by cross composition"
    - "designing fpt algorithms for cut problems using randomized contractions"
    - "solving max r sat above a tight lower bound"

Connected through hyperedge HE-5 (induced by source paper "from acquaintance to best friend forever robust and fine grained inference of social tie strengths"):
  with members:
    - "inferring the strength of social ties a community driven approach"
    - "smoothed analysis of algorithms why the simplex algorithm usually takes polynomial time"

Connected through hyperedge HE-6 (induced by source paper "sampling online social networks by random walk with indirect jumps"):
  with members:
    - "inferring networks of substitutable and complementary products"
    - "leveraging history for faster sampling of online social networks"
    - "efficiently estimating motif statistics of large networks"
    - "triadic measures on graphs the power of wedge sampling"
    - "faster random walks by rewiring online social networks on the fly"
    - "multigraph sampling of online social networks"
    - "estimating and sampling graphs with multidimensional random walks"

Connected through hyperedge HE-7 (induced by source paper "detecting strong ties using network motifs"):
  with members:
    - "triadic measures on graphs the power of wedge sampling"
    - "spectral redemption in clustering sparse networks"
    - "subgraph frequencies mapping the empirical and extremal geography of large graph collections"
    - "modeling social networks with node attributes using the multiplicative attribute graph model"

Connected through hyperedge HE-8 (induced by source paper "effector detection in social networks"):
  with members:
    - "adaptive influence maximization in dynamic social networks"
    - "rooting out the rumor culprit from suspects"
    - "information source detection in the sir model a sample path based approach"
    - "discovering social circles in ego 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)