human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "estimating network structure from unreliable measurements"):
  Abstract: Most empirical studies of networks assume that the network data we are given represent a complete and accurate picture of the nodes and edges in the system of interest, but in real-world situations this is rarely the case. More often the data only specify the network structure imperfectly -- like data in essentially every other area of empirical science, network data are prone to measurement error and noise. At the same time, the data may be richer than simple network measurements, incorporating multiple measurements, weights, lengths or strengths of edges, node or edge labels, or annotations of various kinds. Here we develop a general method for making estimates of network structure and properties using any form of network data, simple or complex, when the data are unreliable, and give example applications to a selection of social and biological networks.

Includes members:
  Member "network structure from rich but noisy data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "improving textual network learning with variational homophilic embeddings")
  Member "reconstructing networks with unknown and heterogeneous errors":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bayesian inference of network structure from information cascades")
  Member "structural inference for uncertain networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "network structure from rich but noisy data")
  Member "network structure inference a survey motivations methods and applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "disentangling interpretable generative parameters of random and real world graphs")
  Member "measuring large scale social networks with high resolution":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "computational human dynamics")
  Member "coauthorship and citation patterns in the physical review":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "predicting research trends with semantic and neural networks with an application in quantum physics")
  Member "friendship networks and social status":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "measuring directed triadic closure with closure coefficients")
  Member "a heuristic approach to author name disambiguation in bibliometrics databases for large scale research assessments":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "comparison of research productivity of italian and norwegian professors and universities")

Question: Please tell me which class the source paper 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)