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: "studying social networks at scale macroscopic anatomy of the twitter social graph"
  Abstract: Twitter is one of the largest social networks using exclusively directed links among accounts. This makes the Twitter social graph much closer to the social graph supporting real life communications than, for instance, Facebook. Therefore, understanding the structure of the Twitter social graph is interesting not only for computer scientists, but also for researchers in other fields, such as sociologists. However, little is known about how the information propagation in Twitter is constrained by its inner structure. In this paper, we present an in-depth study of the macroscopic structure of the Twitter social graph unveiling the highways on which tweets propagate, the specific user activity associated with each component of this macroscopic structure, and the evolution of this macroscopic structure with time for the past 6 years. For this study, we crawled Twitter to retrieve all accounts and all social relationships (follow links) among accounts; the crawl completed in July 2012 with 505 million accounts interconnected by 23 billion links. Then, we present a methodology to unveil the macroscopic structure of the Twitter social graph. This macroscopic structure consists of 8 components defined by their connectivity characteristics. Each component group users with a specific usage of Twitter. For instance, we identified components gathering together spammers, or celebrities. Finally, we present a method to approximate the macroscopic structure of the Twitter social graph in the past, validate this method using old datasets, and discuss the evolution of the macroscopic structure of the Twitter social graph during the past 6 years.

Connected through hyperedge HE-1 (induced by source paper "a systematic identification and analysis of scientists on twitter"):
  with members:
    - "differences in personal and professional tweets of scholars"
    - "astrophysicists on twitter an in depth analysis of tweeting and scientific publication behavior"
    - "do altmetrics correlate with citations extensive comparison of altmetric indicators with citations from a multidisciplinary perspective"
    - "alternative metrics in scientometrics a meta analysis of research into three altmetrics"
    - "do altmetrics point to the broader impact of research an overview of benefits and disadvantages of altmetrics"
    - "how well developed are altmetrics a cross disciplinary analysis of the presence of alternative metrics in scientific publications"
    - "tweeting biomedicine an analysis of tweets and citations in the biomedical literature"
    - "the role of twitter in the life cycle of a scientific publication"

Connected through hyperedge HE-2 (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"
    - "social bootstrapping how pinterest and last fm social communities benefit by borrowing links from facebook"
    - "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-3 (induced by source paper "liverank how to refresh old datasets"):
  with members:
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"

Connected through hyperedge HE-4 (induced by source paper "microblog analysis as a programme of work"):
  with members:
    - "supporting the use of user generated content in journalistic practice"
    - "stance classification in rumours as a sequential task exploiting the tree structure of social media conversations"
    - "analysing how people orient to and spread rumours in social media by looking at conversational threads"
    - "towards detecting rumours in social media"
    - "who watches and shares what on youtube and when using twitter to understand youtube viewership"
    - "evolution of online user behavior during a social upheaval"
    - "traveling trends social butterflies or frequent fliers"
    - "mark my words linguistic style accommodation in social media"

Connected through hyperedge HE-5 (induced by source paper "distribution and dependence of extremes in network sampling processes"):
  with members:
    - "quick detection of nodes with large degrees"
    - "empirical validation of the buckley osthus model for the web host graph degree and edge distributions"

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)