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: "reconstructing networks with unknown and heterogeneous errors"
  Abstract: The vast majority of network datasets contains errors and omissions, although this is rarely incorporated in traditional network analysis. Recently, an increasing effort has been made to fill this methodological gap by developing network reconstruction approaches based on Bayesian inference. These approaches, however, rely on assumptions of uniform error rates and on direct estimations of the existence of each edge via repeated measurements, something that is currently unavailable for the majority of network data. Here we develop a Bayesian reconstruction approach that lifts these limitations by not only allowing for heterogeneous errors, but also for single edge measurements without direct error estimates. Our approach works by coupling the inference approach with structured generative network models, which enable the correlations between edges to be used as reliable uncertainty estimates. Although our approach is general, we focus on the stochastic block model as the basic generative process, from which efficient nonparametric inference can be performed, and yields a principled method to infer hierarchical community structure from noisy data. We demonstrate the efficacy of our approach with a variety of empirical and artificial networks.

Connected through hyperedge HE-1 (induced by source paper "bayesian inference of network structure from information cascades"):
  with members:
    - "super blockers and the effect of network structure on information cascades"
    - "generating connected random graphs"
    - "estimating network structure from unreliable measurements"
    - "dynamic network model from partial observations"
    - "smerc social media event response clustering using textual and temporal information"
    - "dani a fast diffusion aware network inference algorithm"
    - "the flip markov chain for connected regular graphs"
    - "submodular variational inference for network reconstruction"

Connected through hyperedge HE-2 (induced by source paper "democratic summary of public opinions in free response surveys"):
  with members:
    - "network structure from rich but noisy data"
    - "comparative analysis on the selection of number of clusters in community detection"
    - "algorithmic detectability threshold of the stochastic block model"
    - "the computer science and physics of community detection landscapes phase transitions and hardness"
    - "algorithmic infeasibility of community detection in higher order networks"
    - "cross validation estimate of the number of clusters in a network"
    - "estimating the number of communities in a network"
    - "spectral redemption in clustering sparse networks"

Connected through hyperedge HE-3 (induced by source paper "estimating network structure from unreliable measurements"):
  with members:
    - "network structure from rich but noisy data"
    - "structural inference for uncertain networks"
    - "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"

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)