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: "multiresolution consensus clustering in networks"
  Abstract: Networks often exhibit structure at disparate scales. We propose a method for identifying community structure at different scales based on multiresolution modularity and consensus clustering. Our contribution consists of two parts. First, we propose a strategy for sampling the entire range of possible resolutions for the multiresolution modularity quality function. Our approach is directly based on the properties of modularity and, in particular, provides a natural way of avoiding the need to increase the resolution parameter by several orders of magnitude to break a few remaining small communities, necessitating the introduction of ad-hoc limits to the resolution range with standard sampling approaches. Second, we propose a hierarchical consensus clustering procedure, based on a modified modularity, that allows one to construct a hierarchical consensus structure given a set of input partitions. While here we are interested in its application to partitions sampled using multiresolution modularity, this consensus clustering procedure can be applied to the output of any clustering algorithm. As such, we see many potential applications of the individual parts of our multiresolution consensus clustering procedure in addition to using the procedure itself to identify hierarchical structure in networks.

Connected through hyperedge HE-1 (induced by source paper "learning resolution parameters for graph clustering"):
  with members:
    - "a parallel projection method for metric constrained optimization"
    - "correlation clustering generalized"
    - "flow based local graph clustering with better seed set inclusion"
    - "capacity releasing diffusion for speed and locality"
    - "the ground truth about metadata and community detection in networks"
    - "an optimization approach to locally biased graph algorithms"
    - "higher order organization of complex networks"
    - "a simple and strongly local flow based method for cut improvement"

Connected through hyperedge HE-2 (induced by source paper "graph clustering in all parameter regimes"):
  with members:
    - "from louvain to leiden guaranteeing well connected communities"
    - "learning resolution parameters for graph clustering"
    - "correlation clustering generalized"
    - "network clustering via maximizing modularity approximation algorithms and theoretical limits"

Connected through hyperedge HE-3 (induced by source paper "the emergent integrated network structure of scientific research"):
  with members:
    - "the landscape of neuroimage ing research"
    - "equivalence between modularity optimization and maximum likelihood methods for community detection"
    - "quantifying long term scientific impact"
    - "assessing the consistency of community structure in complex networks"

Connected through hyperedge HE-4 (induced by source paper "regularized stochastic block model for robust community detection in complex networks"):
  with members:
    - "the ground truth about metadata and community detection in networks"
    - "equivalence between modularity optimization and maximum likelihood methods for community detection"

Connected through hyperedge HE-5 (induced by source paper "unifying sparsest cut cluster deletion and modularity clustering objectives with correlation clustering"):
  with members:
    - "large scale correlation clustering optimization"

Connected through hyperedge HE-6 (induced by source paper "a framework for the construction of generative models for mesoscale structure in multilayer networks"):
  with members:
    - "community discovery in dynamic networks a survey"
    - "random graph models for dynamic networks"
    - "the many facets of community detection in complex networks"
    - "the geometric block model"
    - "block models and personalized pagerank"
    - "community detection link prediction and layer interdependence in multilayer networks"
    - "the ground truth about metadata and community detection in networks"
    - "scout simultaneous time segmentation and community detection in dynamic 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)