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: "fast matrix computations for pairwise and columnwise commute times and katz scores"
  Abstract: Abstract We explore methods for approximating the commute time and Katz score between a pair of nodes. These methods are based on the approach of matrices, moments, and quadrature developed in the numerical linear algebra community. They rely on the Lanczos process and provide upper and lower bounds on an estimate of the pairwise scores. We also explore methods to approximate the commute times and Katz scores from a node to all other nodes in the graph. Here, our approach for the commute times is based on a variation of the conjugate gradient algorithm, and it provides an estimate of all the diagonals of the inverse of a matrix. Our technique for the Katz scores is based on exploiting an empirical localization property of the Katz matrix. We adapt algorithms used for personalized PageRank computing to these Katz scores and theoretically show that this approach is convergent. We evaluate these methods on 17 real-world graphs ranging in size from 1000 to 1,000,000 nodes. Our results show that our pairwise c...

Connected through hyperedge HE-1 (induced by source paper "fast computation of katz index for efficient processing of link prediction queries"):
  with members:
    - "a tractable approach to finding closest truncated commute time neighbors in large graphs"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"

Connected through hyperedge HE-2 (induced by source paper "localization in seeded pagerank"):
  with members:
    - "bidirectional pagerank estimation from average case to worst case"
    - "fast ppr scaling personalized pagerank estimation for large graphs"
    - "a scalable generative graph model with community structure"
    - "pagerank beyond the web"
    - "sublinear algorithms for local graph centrality estimation"
    - "quick detection of nodes with large degrees"
    - "a sequential algorithm for generating random graphs"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-3 (induced by source paper "overlapping community detection using neighborhood inflated seed expansion"):
  with members:
    - "overlapping community detection in networks the state of the art and comparative study"
    - "demon a local first discovery method for overlapping communities"
    - "detect overlapping and hierarchical community structure in networks"
    - "a local spectral method for graphs with applications to improving graph partitions and exploring data graphs locally"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-4 (induced by source paper "strong localization in personalized pagerank vectors"):
  with members:
    - "heat kernel based community detection"
    - "quick detection of nodes with large degrees"
    - "a sequential algorithm for generating random graphs"

Connected through hyperedge HE-5 (induced by source paper "heat kernel based community detection"):
  with members:
    - "flow based algorithms for local graph clustering"
    - "programming with personalized pagerank a locally groundable first order probabilistic logic"
    - "defining and evaluating network communities based on ground truth"
    - "implementing regularization implicitly via approximate eigenvector computation"
    - "google matrix dynamical attractors and ulam networks"
    - "fast incremental and personalized pagerank"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-6 (induced by source paper "sublinear column wise actions of the matrix exponential on social networks"):
  with members:
    - "heat kernel based community detection"
    - "revisiting asynchronous linear solvers provable convergence rate through randomization"
    - "defining and evaluating network communities based on ground truth"
    - "approximating the exponential the lanczos method and an tilde o m time spectral algorithm for balanced separator"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"

Connected through hyperedge HE-7 (induced by source paper "multi scale link prediction"):
  with members:
    - "supervised random walks predicting and recommending links in social networks"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-8 (induced by source paper "neighborhoods are good communities"):
  with members:
    - "efficient triangle counting in large graphs via degree based vertex partitioning"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"

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.SI(Social and Information Networks)