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: "distribution of pagerank mass among principle components of the web"
  Abstract: We study the PageRank mass of principal components in a bow-tie Web Graph, as a function of the damping factor c. Using a singular perturbation approach, we show that the PageRank share of IN and SCC components remains high even for very large values of the damping factor, in spite of the fact that it drops to zero when c goes to one. However, a detailed study of the OUT component reveals the presence ``dead-ends'' (small groups of pages linking only to each other) that receive an unfairly high ranking when c is close to one. We argue that this problem can be mitigated by choosing c as small as 1/2.

Connected through hyperedge HE-1 (induced by source paper "mean field analysis of personalized pagerank with implications for local graph clustering"):
  with members:
    - "block models and personalized pagerank"
    - "the power of side information in subgraph detection"
    - "think locally act locally detection of small medium sized and large communities in large networks"
    - "random walks and evolving sets faster convergences and limitations"
    - "phase transitions in semisupervised clustering of sparse networks"
    - "a local clustering algorithm for massive graphs and its application to nearly linear time graph partitioning"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"
    - "nearly linear time algorithms for graph partitioning graph sparsification and solving linear systems"

Connected through hyperedge HE-2 (induced by source paper "random surfing without teleportation"):
  with members:
    - "top n recommendations in the presence of sparsity an ncd based approach"

Connected through hyperedge HE-3 (induced by source paper "google matrix dynamical attractors and ulam networks"):
  with members:
    - "delocalization transition for the google matrix"

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.NI(Networking and Internet Architecture)