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: "structural inference for uncertain networks"
  Abstract: In the study of networked systems such as biological, technological, and social networks the available data are often uncertain. Rather than knowing the structure of a network exactly, we know the connections between nodes only with a certain probability. In this paper we develop methods for the analysis of such uncertain data, focusing particularly on the problem of community detection. We give a principled maximum-likelihood method for inferring community structure and demonstrate how the results can be used to make improved estimates of the true structure of the network. Using computer-generated benchmark networks we demonstrate that our methods are able to reconstruct known communities more accurately than previous approaches based on data thresholding. We also give an example application to the detection of communities in a protein-protein interaction network.

Connected through hyperedge HE-1 (induced by source paper "estimating network structure from unreliable measurements"):
  with members:
    - "network structure from rich but noisy data"
    - "reconstructing networks with unknown and heterogeneous errors"
    - "network structure inference a survey motivations methods and applications"
    - "measuring large scale social networks with high resolution"
    - "coauthorship and citation patterns in the physical review"
    - "friendship networks and social status"
    - "a heuristic approach to author name disambiguation in bibliometrics databases for large scale research assessments"

Connected through hyperedge HE-2 (induced by source paper "network structure from rich but noisy data"):
  with members:
    - "from relational data to graphs inferring significant links using generalized hypergeometric ensembles"
    - "network structure inference a survey motivations methods and applications"
    - "measuring large scale social networks with high resolution"
    - "a heuristic approach to author name disambiguation in bibliometrics databases for large scale research assessments"

Connected through hyperedge HE-3 (induced by source paper "reconstructing networks with unknown and heterogeneous errors"):
  with members:
    - "network structure from rich but noisy data"
    - "structure and inference in annotated networks"
    - "generalized communities in networks"
    - "discovering social circles in ego 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)