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: "cluster analysis for a scale free folksodriven structure network"
  Abstract: Folksonomy is said to provide a democratic tagging system that reflects the opinions of the general public, but it is not a classification system and it is hard to make sense of. It would be necessary to share a representation of contexts by all the users to develop a social and collaborative matching. The solution could be to help the users to choose proper tags thanks to a dynamical driven system of folksonomy that could evolve during the time. This paper uses a cluster analysis to measure a new concept of a structure called "Folksodriven", which consists of tags, source and time. Many approaches include in their goals the use of folksonomy that could evolve during time to evaluate characteristics. This paper describes an alternative where the goal is to develop a weighted network of tags where link strengths are based on the frequencies of tag co-occurrence, and studied the weight distributions and connectivity correlations among nodes in this network. The paper proposes and analyzes the network structure of the Folksodriven tags thought as folksonomy tags suggestions for the user on a dataset built on chosen websites. It is observed that the hypergraphs of the Folksodriven are highly connected and that the relative path lengths are relatively low, facilitating thus the serendipitous discovery of interesting contents for the users. Then its characteristics, Clustering Coefficient, is compared with random networks. The goal of this paper is a useful analysis of the use of folksonomies on some well known and extensive web sites with real user involvement. The advantages of the new tagging method using folksonomy are on a new interesting method to be employed by a knowledge management system. #R##N#*** This paper has been accepted to the International Conference on Social Computing and its Applications (SCA 2011) - Sydney Australia, 12-14 December 2011 ***

Connected through hyperedge HE-1 (induced by source paper "emergent behaviors from folksonomy driven interactions"):
  with members:
    - "automatic data deformation analysis on evolving folksonomy driven environment"
    - "function of forgetfulness for the tedium of oblivion on liquidity of ontology matching"
    - "liquidity on web dynamic network"
    - "elasticity on ontology matching of folksodriven structure network"
    - "faceted semantic search for personalized social search"
    - "folksodriven structure network"
    - "search for hidden knowledge in collective intelligence dealing indeterminacy ontology of folksonomy with linguistic pragmatics and quantum logic"
    - "ontology temporal evolution for multi entity bayesian networks under exogenous and endogenous semantic updating"

Connected through hyperedge HE-2 (induced by source paper "elasticity on ontology matching of folksodriven structure network"):
  with members:
    - "folksodriven structure network"
    - "ontology temporal evolution for multi entity bayesian networks under exogenous and endogenous semantic updating"

Connected through hyperedge HE-3 (induced by source paper "intelligent interface architectures for folksonomy driven structure network"):
  with members:
    - "elasticity on ontology matching of folksodriven structure network"
    - "ontology temporal evolution for multi entity bayesian networks under exogenous and endogenous semantic updating"

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)