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: "yasca a collective intelligence approach for community detection in complex networks"
  Abstract: Rushed Kanawati, University of paris Sorbonne Cit´e1. INTRODUCTIONComplex networks are frequently used for modeling interactions in real-world systems in diverse ar-eas, such as sociology, biology, information spreading and exchanging, scientometrics and many otherdifferent areas. One key topological feature of real-world complex networks is that nodes are arrangedin tightly knit groups that are loosely connected one to each other. Such groups are called communities.Nodes composing a community are generally admitted to share common proprieties and/or be involvedin a same function and/or having a same role. Hence, unfolding the community structure of a networkcould give us much insights about the overall structure a complex network.We distinguish between two different problems: partitioning the whole graph into (eventually over-leaping) communities [Fortunato 2010] and identifying ego-centered communities for a given querynode [Kanawati 2014a]. In this work we propose a new algorithm, YASCA, that use local communityidentiﬁcation in order to compute a global graph partition into communities. The algorithm belongsto the seed centric algorithms family [Kanawati 2014b]. The basic idea of seed centric algorithms isto select a set of nodes (i.e. seeds) around which communities are constructed. Being based on localcomputations, these approaches are very attractive to deal with large-scale and/or dynamic networks.Different algorithms apply different policies for seed selection and for community construction aroundseeds. The number of seed nodes can be pre-determined [Khorasgani et al. 2010] or computed by theapproach itself [Kanawati 2011]. The seed selection process can be : random [Khorasgani et al. 2010] orinformed [Kanawati 2011]. The community construction can be made applying consensus techniques,expansion techniques or agglomeration techniques [Kanawati 2011]. Next we propose an original seedcentric approach that apply an ensemble clustering approach [Strehl and Ghosh 2003] to differentnetwork partitions derived from ego-centered communities computed for each selected seed.2. YASCA: THE PROPOSED ALGORITHMThe YASCA

Connected through hyperedge HE-1 (induced by source paper "ensemble based discovery of disjoint overlapping and fuzzy community structures in networks"):
  with members:
    - "ensemble based algorithms to detect disjoint and overlapping communities in networks"
    - "permanence and community structure in complex networks"
    - "metrics for community analysis a survey"
    - "think locally act locally detection of small medium sized and large communities in large networks"
    - "personalized pagerank solution paths"
    - "leveraging disjoint communities for detecting overlapping community structure"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "labelrankt incremental community detection in dynamic networks via label propagation"

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)