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: "a nonlinear spectral method for core periphery detection in networks"
  Abstract: We derive and analyse a new iterative algorithm for detecting network core--periphery structure. Using techniques in nonlinear Perron-Frobenius theory, we prove global convergence to the unique solution of a relaxed version of a natural discrete optimization problem. On sparse networks, the cost of each iteration scales linearly with the number of nodes, making the algorithm feasible for large-scale problems. We give an alternative interpretation of the algorithm from the perspective of maximum likelihood reordering of a new logistic core--periphery random graph model. This viewpoint also gives a new basis for quantitatively judging a core--periphery detection algorithm. We illustrate the algorithm on a range of synthetic and real networks, and show that it offers advantages over the current state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "nonlocal pagerank"):
  with members:
    - "a framework for second order eigenvector centralities and clustering coefficients"
    - "three hypergraph eigenvector centralities"
    - "a modularity based spectral method for simultaneous community and anti community detection"
    - "the spacey random walk a stochastic process for higher order data"
    - "higher order organization of complex networks"
    - "localization and centrality in networks"
    - "total communicability as a centrality measure"

Connected through hyperedge HE-2 (induced by source paper "total variation based community detection using a nonlinear optimization approach"):
  with members:
    - "community detection in networks via nonlinear modularity eigenvectors"
    - "community detection in political twitter networks using nonnegative matrix factorization methods"
    - "a method based on total variation for network modularity optimization using the mbo scheme"
    - "advanced coarsening schemes for graph partitioning"
    - "learning with submodular functions a convex optimization perspective"
    - "an inverse power method for nonlinear eigenproblems with applications in 1 spectral clustering and sparse pca"

Connected through hyperedge HE-3 (induced by source paper "core periphery structure in directed networks"):
  with members:
    - "detection of core periphery structure in networks using spectral methods and geodesic paths"
    - "structure and overlaps of communities in networks"

Connected through hyperedge HE-4 (induced by source paper "random spatial network models with core periphery structure"):
  with members:
    - "higher order clustering in networks"
    - "found graph data and planted vertex covers"
    - "coin flipping ball dropping and grass hopping for generating random graphs from matrices of edge probabilities"
    - "classifying developers into core and peripheral an empirical study on count and network metrics"
    - "detection of core periphery structure in networks using spectral methods and geodesic paths"
    - "identification of core periphery structure in networks"
    - "finding the hierarchy of dense subgraphs using nucleus decompositions"
    - "community detection in networks with node attributes"

Connected through hyperedge HE-5 (induced by source paper "community detection in networks via nonlinear modularity eigenvectors"):
  with members:
    - "an inverse power method for nonlinear eigenproblems with applications in 1 spectral clustering and sparse pca"

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)