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: "identifying topical twitter communities via user list aggregation"
  Abstract: A particular challenge in the area of social media analysis is how to find communities within a larger network of social interactions. Here a community may be a group of microblogging users who post content on a coherent topic, or who are associated with a specific event or news story. Twitter provides the ability to curate users into lists, corresponding to meaningful topics or themes. Here we describe an approach for crowdsourcing the list building efforts of many different Twitter users, in order to identify topical communities. This approach involves the use of ensemble community finding to produce stable groupings of user lists, and by extension, individual Twitter users. We examine this approach in the context of a case study surrounding the detection of communities on Twitter relating to the London 2012 Olympics.

Connected through hyperedge HE-1 (induced by source paper "analysis of multiview legislative networks with structured matrix factorization does twitter influence translate to the real world"):
  with members:
    - "producing a unified graph representation from multiple social network views"
    - "structural and functional discovery in dynamic networks with non negative matrix factorization"
    - "community detection in networks with node attributes"
    - "social networks that matter twitter under the microscope"

Connected through hyperedge HE-2 (induced by source paper "uncovering the wider structure of extreme right communities spanning popular online networks"):
  with members:
    - "an analysis of interactions within and between extreme right communities in social media"
    - "recipe recommendation using ingredient networks"

Connected through hyperedge HE-3 (induced by source paper "link prediction with social vector clocks"):
  with members:
    - "twittercrowds techniques for exploring topic and sentiment in microblogging data"

Connected through hyperedge HE-4 (induced by source paper "discovering latent patterns from the analysis of user curated movie lists"):
  with members:
    - "aggregating content and network information to curate twitter user lists"

Connected through hyperedge HE-5 (induced by source paper "an analysis of interactions within and between extreme right communities in social media"):
  with members:
    - "supporting the curation of twitter user lists"

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)