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 bootstrapping how pinterest and last fm social communities benefit by borrowing links from facebook"
  Abstract: How does one develop a new online community that is highly engaging to each user and promotes social interaction? A number of websites offer friend-finding features that help users bootstrap social networks on the website by copying links from an established network like Facebook or Twitter. This paper quantifies the extent to which such social bootstrapping is effective in enhancing a social experience of the website. First, we develop a stylised analytical model that suggests that copying tends to produce a giant connected component (i.e., a connected community) quickly and preserves properties such as reciprocity and clustering, up to a linear multiplicative factor. Second, we use data from two websites, Pinterest and Last.fm, to empirically compare the subgraph of links copied from Facebook to links created natively. We find that the copied subgraph has a giant component, higher reciprocity and clustering, and confirm that the copied connections see higher social interactions. However, the need for copying diminishes as users become more active and influential. Such users tend to create links natively on the website, to users who are more similar to them than their Facebook friends. Our findings give new insights into understanding how bootstrapping from established social networks can help engage new users by enhancing social interactivity.

Connected through hyperedge HE-1 (induced by source paper "providing a link prediction model based on structural and homophily similarity in social networks"):
  with members:
    - "link prediction in multiplex networks based on interlayer similarity"
    - "qanet tensor decomposition approach for query based anomaly detection in heterogeneous information networks"
    - "link prediction with node clustering coefficient"
    - "supervised random walks predicting and recommending links in social networks"

Connected through hyperedge HE-2 (induced by source paper "woman metal white vs man dress shorts combining social temporal and image signals to understand popularity of pinterest fashion boards"):
  with members:
    - "sequential prediction of social media popularity with deep temporal context networks"
    - "cats and captions vs creators and the clock comparing multimodal content to context in predicting relative popularity"
    - "cultural diffusion and trends in facebook photographs"
    - "evolution of fashion brands on twitter and instagram"
    - "pinterest board recommendation for twitter users"
    - "densecap fully convolutional localization networks for dense captioning"

Connected through hyperedge HE-3 (induced by source paper "systems applications of social networks"):
  with members:
    - "a survey of signed network mining in social media"
    - "the architecture of innovation tracking face to face interactions with ubicomp technologies"
    - "geo spotting mining online location based services for optimal retail store placement"
    - "social turing tests crowdsourcing sybil detection"
    - "explore what if scenarios with song social network write generator"
    - "uncovering social network sybils in the wild"
    - "empirical comparison of algorithms for network community detection"
    - "supervised random walks predicting and recommending links in social networks"

Connected through hyperedge HE-4 (induced by source paper "wearing many social hats how different are your different social network personae"):
  with members:
    - "mytweet via instagram exploring user behaviour across multiple social networks"
    - "caffe convolutional architecture for fast feature embedding"
    - "big questions for social media big data representativeness validity and other methodological pitfalls"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "understanding service integration of online social networks a data driven study"):
  with members:
    - "xgboost a scalable tree boosting system"
    - "snap a general purpose network analysis and graph mining library"
    - "studying social networks at scale macroscopic anatomy of the twitter social graph"
    - "multi scale dynamics in a massive online social network"
    - "google or google dissecting the evolution of the new osn in its first year"
    - "the anatomy of the facebook social graph"

Connected through hyperedge HE-6 (induced by source paper "characterization of cross posting activity for professional users across facebook twitter and google"):
  with members:
    - "mytweet via instagram exploring user behaviour across multiple social networks"
    - "google or google dissecting the evolution of the new osn in its first year"
    - "the anatomy of the facebook social graph"
    - "four degrees of separation"

Connected through hyperedge HE-7 (induced by source paper "spreading processes in multilayer networks"):
  with members:
    - "clustering attributed graphs models measures and methods"
    - "multidimensional epidemic thresholds in diffusion processes over interdependent networks"
    - "multiple routes transmitted epidemics on multiplex networks"
    - "measuring large scale social networks with high resolution"
    - "diffusion of innovations over multiplex social networks"
    - "cooperation optimized design for information dissemination in vehicular networks using evolutionary game theory"
    - "exploiting cellular data for disease containment and information campaigns strategies in country wide epidemics"
    - "combinatorial analysis of multiple networks"

Connected through hyperedge HE-8 (induced by source paper "a multilayer approach to multiplexity and link prediction in online geo social networks"):
  with members:
    - "keep your friends close and your facebook friends closer a multiplex network approach to the analysis of offline and online social ties"
    - "measuring large scale social networks with high resolution"
    - "multi layered social networks"
    - "supervised random walks predicting and recommending links 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)