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 "democratic summary of public opinions in free response surveys"):
  Abstract: Social surveys have been widely used as a method of obtaining public opinion. Sometimes, it is more ideal to collect opinions by presenting questions in free-response formats than in multiple-choice formats. Despite their advantages, free-response questions are rarely used in practice because they usually require manual analysis. Therefore, classification of free-format texts can present a formidable task in large-scale surveys and can be influenced by the interpretation of analysts. In this study, we propose a network-based survey framework in which responses are automatically classified in a statistically principled manner. This can be achieved because, in addition to the text, similarities among responses are also assessed by each respondent. We demonstrate our approach using a poll on the 2016 US presidential election and a survey taken by graduates of a particular university. The proposed approach helps analysts interpret the underlying semantics of responses in large-scale surveys.

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 "comparative analysis on the selection of number of clusters in community detection":
    also connected through other hyperedges:
      (none)
  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 "algorithmic detectability threshold of the stochastic block model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mean field theory of graph neural networks in graph partitioning")
  Member "the computer science and physics of community detection landscapes phase transitions and hardness":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mean field theory of graph neural networks in graph partitioning")
  Member "algorithmic infeasibility of community detection in higher order networks":
    also connected through other hyperedges:
      (none)
  Member "cross validation estimate of the number of clusters in a network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "comparative analysis on the selection of number of clusters in community detection")
  Member "estimating the number of communities in a network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mean field theory of graph neural networks in graph partitioning")

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)