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: "hierarchical quasi clustering methods for asymmetric networks"
  Abstract: This paper introduces hierarchical quasi-clustering methods, a generalization of hierarchical clustering for asymmetric networks where the output structure preserves the asymmetry of the input data. We show that this output structure is equivalent to a finite quasi-ultrametric space and study admissibility with respect to two desirable properties. We prove that a modified version of single linkage is the only admissible quasi-clustering method. Moreover, we show stability of the proposed method and we establish invariance properties fulfilled by it. Algorithms are further developed and the value of quasi-clustering analysis is illustrated with a study of internal migration within United States.

Connected through hyperedge HE-1 (induced by source paper "the metric space of networks"):
  with members:
    - "distances and isomorphism between networks and the stability of network invariants"
    - "axiomatic construction of hierarchical clustering in asymmetric networks"

Connected through hyperedge HE-2 (induced by source paper "distances and isomorphism between networks and the stability of network invariants"):
  with members:
    - "the metric space of networks"
    - "spectral descriptors for graph matching"
    - "stable and informative spectral signatures for graph matching"
    - "deltacon a principled massive graph similarity function"
    - "axiomatic construction of hierarchical clustering in asymmetric networks"
    - "substructure discovery using minimum description length and background knowledge"

Connected through hyperedge HE-3 (induced by source paper "a mathematical theory for clustering in metric spaces"):
  with members:
    - "a generalized and adaptive method for community detection"
    - "spectral redemption in clustering sparse networks"
    - "community detection thresholds and the weak ramanujan property"
    - "a uniqueness theorem for clustering"
    - "scalable k means"
    - "the numpy array a structure for efficient numerical computation"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-4 (induced by source paper "hierarchical overlapping clustering of network data using cut metrics"):
  with members:
    - "functorial hierarchical clustering with overlaps"
    - "authorship attribution through function word adjacency networks"
    - "axiomatic construction of hierarchical clustering in asymmetric networks"
    - "detect overlapping and hierarchical community structure in networks"
    - "overlapping multihop clustering for wireless sensor networks"

Connected through hyperedge HE-5 (induced by source paper "admissible hierarchical clustering methods and algorithms for asymmetric networks"):
  with members:
    - "hierarchical clustering of asymmetric networks"
    - "axioms for graph clustering quality functions"
    - "axiomatic construction of hierarchical clustering in asymmetric networks"
    - "a uniqueness theorem for clustering"

Connected through hyperedge HE-6 (induced by source paper "excisive hierarchical clustering methods for network data"):
  with members:
    - "axioms for graph clustering quality functions"
    - "axiomatic construction of hierarchical clustering in asymmetric networks"
    - "a uniqueness theorem for clustering"

Connected through hyperedge HE-7 (induced by source paper "hierarchical clustering of asymmetric networks"):
  with members:
    - "axiomatic construction of hierarchical clustering in asymmetric networks"
    - "a uniqueness theorem for clustering"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-8 (induced by source paper "axiomatic construction of hierarchical clustering in asymmetric networks"):
  with members:
    - "a uniqueness theorem for clustering"
    - "a tutorial on spectral clustering"

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.LG(Machine Learning)