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 "stochastic block models are a discrete surface tension"):
  Abstract: Author(s): Boyd, Zachary M; Porter, Mason A; Bertozzi, Andrea L | Abstract: Networks, which represent agents and interactions between them, arise in myriad applications throughout the sciences, engineering, and even the humanities. To understand large-scale structure in a network, a common task is to cluster a network's nodes into sets called "communities", such that there are dense connections within communities but sparse connections between them. A popular and statistically principled method to perform such clustering is to use a family of generative models known as stochastic block models (SBMs). In this paper, we show that maximum likelihood estimation in an SBM is a network analog of a well-known continuum surface-tension problem that arises from an application in metallurgy. To illustrate the utility of this relationship, we implement network analogs of three surface-tension algorithms, with which we successfully recover planted community structure in synthetic networks and which yield fascinating insights on empirical networks that we construct from hyperspectral videos.

Includes members:
  Member "community detection in networks via nonlinear modularity eigenvectors":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "total variation based community detection using a nonlinear optimization approach")
  Member "unifying sparsest cut cluster deletion and modularity clustering objectives with correlation clustering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "correlation clustering generalized")
  Member "the computer science and physics of community detection landscapes phase transitions and hardness":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mean field theory of graph neural networks in graph partitioning")
  Member "unsupervised classification in hyperspectral imagery with nonlocal total variation and primal dual hybrid gradient algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "punch positive unlabelled classification based information retrieval in hyperspectral images")
  Member "uncertainty quantification in graph based classification of high dimensional data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ensemble kalman inversion a derivative free technique for machine learning tasks")
  Member "efficient method for estimating the number of communities in a network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "consistency of community structure in complex networks")
  Member "block models and personalized pagerank":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "diffusion improves graph learning")
  Member "the ground truth about metadata and community detection in networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "parameterized objectives and algorithms for clustering bipartite graphs and hypergraphs")

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)