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: "detection of core periphery structure in networks using spectral methods and geodesic paths"
  Abstract: We introduce several novel and computationally efficient methods for detecting “core–periphery structure” in networks. Core–periphery structure is a type of mesoscale structure that consists of densely connected core vertices and sparsely connected peripheral vertices. Core vertices tend to be well-connected both among themselves and to peripheral vertices, which tend not to be well-connected to other vertices. Our first method, which is based on transportation in networks, aggregates information from many geodesic paths in a network and yields a score for each vertex that reflects the likelihood that that vertex is a core vertex. Our second method is based on a low-rank approximation of a network's adjacency matrix, which we express as a perturbation of a tensor-product matrix. Our third approach uses the bottom eigenvector of the random-walk Laplacian to infer a coreness score and a classification into core and peripheral vertices. We also design an objective function to (1) help classify vertices into core or peripheral vertices and (2) provide a goodness-of-fit criterion for classifications into core versus peripheral vertices. To examine the performance of our methods, we apply our algorithms to both synthetically generated networks and a variety of networks constructed from real-world data sets.

Connected through hyperedge HE-1 (induced by source paper "core periphery structure in directed networks"):
  with members:
    - "a nonlinear spectral method for core periphery detection in networks"
    - "structure and overlaps of communities in networks"

Connected through hyperedge HE-2 (induced by source paper "convexity in complex networks"):
  with members:
    - "corrected overlap weight and clustering coefficient"
    - "higher order organization of complex networks"
    - "representing higher order dependencies in networks"
    - "clustering scientific publications based on citation relations a systematic comparison of different methods"
    - "identification of core periphery structure in networks"
    - "network lasso clustering and optimization in large graphs"
    - "core periphery structure in networks"
    - "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 "random spatial network models with core periphery structure"):
  with members:
    - "higher order clustering in networks"
    - "a nonlinear spectral method for core periphery detection 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"
    - "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-4 (induced by source paper "core periphery in networks an axiomatic approach"):
  with members:
    - "distributed computing on core periphery networks axiom based design"
    - "the slashdot zoo mining a social network with negative edges"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

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.DM(Discrete Mathematics)