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: "multiscale network generation"
  Abstract: Networks are widely used in science and technology to represent relationships between entities, such as social or ecological links between organisms, enzymatic interactions in metabolic systems, or computer infrastructure. Statistical analyses of networks can provide critical insights into the structure, function, dynamics, and evolution of those systems. However, the structures of real-world networks are often not known completely, and they may exhibit considerable variation so that no single network is sufficiently representative of a system. In such situations, researchers may turn to proxy data from related systems, sophisticated methods for network inference, or synthetic networks. Here, we introduce a flexible method for synthesizing realistic ensembles of networks starting from a known network, through a series of mappings that coarsen and later refine the network structure by randomized editing. The method, MUSKETEER, preserves structural properties with minimal bias, including unknown or unspecified features, while introducing realistic variability at multiple scales. Using examples from several domains, we show that MUSKETEER produces the intended stochasticity while achieving greater fidelity across a suite of network properties than do other commonly used network generation algorithms.

Connected through hyperedge HE-1 (induced by source paper "centralities for networks with consumable resources"):
  with members:
    - "relaxation based coarsening for multilevel hypergraph partitioning"
    - "utility maximization framework for opportunistic wireless electric vehicle charging"
    - "generating realistic scaled complex networks"
    - "lurking in social networks topology based analysis and ranking methods"
    - "rethinking centrality the role of dynamical processes in social network analysis"

Connected through hyperedge HE-2 (induced by source paper "multiscale planar graph generation"):
  with members:
    - "single and multi level network sparsification by algebraic distance"
    - "generating realistic scaled complex networks"
    - "generating scaled replicas of real world complex networks"
    - "networkit an interactive tool suite for high performance network analysis"
    - "a multilevel bilinear programming algorithm for the vertex separator problem"
    - "recent advances in graph partitioning"
    - "community structure and scale free collections of erdős renyi graphs"
    - "advanced coarsening schemes for graph partitioning"

Connected through hyperedge HE-3 (induced by source paper "on rich clubs of path based centralities in networks"):
  with members:
    - "locally estimating core numbers"
    - "truss decomposition in massive networks"
    - "core periphery structure in networks"
    - "the rich club phenomenon in the internet topology"
    - "accurately modeling the internet topology"

Connected through hyperedge HE-4 (induced by source paper "generating realistic scaled complex networks"):
  with members:
    - "engineering parallel algorithms for community detection in massive networks"
    - "snap a general purpose network analysis and graph mining library"
    - "structure preserving sparsification methods for social networks"
    - "generating scaled replicas of real world complex networks"
    - "drawing large graphs by multilevel maxent stress optimization"
    - "networkit a tool suite for large scale complex network analysis"
    - "a scalable generative graph model with community structure"
    - "tree based coarsening and partitioning of complex networks"

Connected through hyperedge HE-5 (induced by source paper "generating scaled replicas of real world complex networks"):
  with members:
    - "engineering parallel algorithms for community detection in massive networks"
    - "structure preserving sparsification methods for social networks"
    - "networkit a tool suite for large scale complex network analysis"
    - "a scalable generative graph model with community structure"
    - "social structure of facebook networks"

Connected through hyperedge HE-6 (induced by source paper "dynamic clustering in social networks using louvain and infomap method"):
  with members:
    - "predicting community evolution in social networks"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"
    - "rhythms of social interaction messaging within a massive online network"

Connected through hyperedge HE-7 (induced by source paper "recent advances in graph partitioning"):
  with members:
    - "on the parameterized complexity of computing balanced partitions in graphs"
    - "semi external algorithms for graph partitioning and clustering"
    - "tree based coarsening and partitioning of complex networks"
    - "a multilevel bilinear programming algorithm for the vertex separator problem"
    - "parallel graph partitioning for complex networks"
    - "algorithms for mapping parallel processes onto grid and torus architectures"
    - "partitioning complex networks via size constrained clustering"
    - "community detection and graph partitioning"

Connected through hyperedge HE-8 (induced by source paper "fast response to infection spread and cyber attacks on large scale networks"):
  with members:
    - "advanced coarsening schemes for graph partitioning"
    - "relaxation based coarsening and multiscale graph organization"
    - "mapping the gnutella network properties of large scale peer to peer systems and implications for system design"

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)