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 "mean field analysis of personalized pagerank with implications for local graph clustering"):
  Abstract: We analyse a mean-field model of Personalized PageRank (PPR) on the Erdős–Renyi (ER) random graph containing a denser planted ER subgraph. We investigate the regimes where the values of PPR concentrate around the mean-field value. We also study the optimization of the damping factor, the only parameter in PPR. Our theoretical results help to understand the applicability of PPR and its limitations for local graph clustering.

Includes members:
  Member "block models and personalized pagerank":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "diffusion improves graph learning")
  Member "the power of side information in subgraph detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "exit analysis for community detection")
  Member "think locally act locally detection of small medium sized and large communities in large networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "migration networks applications of network analysis to large scale human mobility")
  Member "random walks and evolving sets faster convergences and limitations":
    also connected through other hyperedges:
      (none)
  Member "phase transitions in semisupervised clustering of sparse networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "renyi entropy bounds on the active learning cost performance tradeoff")
  Member "a local clustering algorithm for massive graphs and its application to nearly linear time graph partitioning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "compressive sensing for cut improvement and local clustering")
  Member "community structure in large networks natural cluster sizes and the absence of large well defined clusters":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "certified robustness of community detection against adversarial structural perturbation via randomized smoothing")
  Member "distribution of pagerank mass among principle components of the web":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "random surfing without teleportation")

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)