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: "axiomatic construction of hierarchical clustering in asymmetric networks"
  Abstract: This paper considers networks where relationships between nodes are represented by directed dissimilarities. The goal is to study methods for the determination of hierarchical clusters, i.e., a family of nested partitions indexed by a connectivity parameter, induced by the given dissimilarity structures. Our construction of hierarchical clustering methods is based on defining admissible methods to be those methods that abide by the axioms of value - nodes in a network with two nodes are clustered together at the maximum of the two dissimilarities between them - and transformation - when dissimilarities are reduced, the network may become more clustered but not less. Several admissible methods are constructed and two particular methods, termed reciprocal and nonreciprocal clustering, are shown to provide upper and lower bounds in the space of admissible methods. Alternative clustering methodologies and axioms are further considered. Allowing the outcome of hierarchical clustering to be asymmetric, so that it matches the asymmetry of the original data, leads to the inception of quasi-clustering methods. The existence of a unique quasi-clustering method is shown. Allowing clustering in a two-node network to proceed at the minimum of the two dissimilarities generates an alternative axiomatic construction. There is a unique clustering method in this case too. The paper also develops algorithms for the computation of hierarchical clusters using matrix powers on a min-max dioid algebra and studies the stability of the methods proposed. We proved that most of the methods introduced in this paper are such that similar networks yield similar hierarchical clustering results. Algorithms are exemplified through their application to networks describing internal migration within states of the United States (U.S.) and the interrelation between sectors of the U.S. economy.

Connected through hyperedge HE-1 (induced by source paper "motivic clustering schemes for directed graphs"):
  with members:
    - "distances and isomorphism between networks and the stability of network invariants"
    - "excisive hierarchical clustering methods for network data"

Connected through hyperedge HE-2 (induced by source paper "metric representations of networks a uniqueness result"):
  with members:
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "node2vec scalable feature learning for networks"
    - "hierarchical clustering of asymmetric networks"
    - "semi supervised classification with graph convolutional networks"
    - "admissible hierarchical clustering methods and algorithms for asymmetric networks"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-3 (induced by source paper "the metric space of networks"):
  with members:
    - "distances and isomorphism between networks and the stability of network invariants"
    - "hierarchical quasi clustering methods for asymmetric networks"

Connected through hyperedge HE-4 (induced by source paper "stationary graph processes and spectral estimation"):
  with members:
    - "blind identification of graph filters"
    - "signal processing on graphs causal modeling of un structured data"
    - "stationary signal processing on graphs"
    - "reconstruction of graph signals through percolation from seeding nodes"
    - "network topology inference from spectral templates"
    - "network topology identification from spectral templates"
    - "subsampling for graph power spectrum estimation"
    - "distributed linear network operators using graph filters"

Connected through hyperedge HE-5 (induced by source paper "distances and isomorphism between networks and the stability of network invariants"):
  with members:
    - "the metric space of networks"
    - "hierarchical quasi clustering methods for asymmetric networks"
    - "spectral descriptors for graph matching"
    - "stable and informative spectral signatures for graph matching"
    - "deltacon a principled massive graph similarity function"
    - "substructure discovery using minimum description length and background knowledge"

Connected through hyperedge HE-6 (induced by source paper "convergence of hierarchical clustering and persistent homology methods on directed networks"):
  with members:
    - "distances and isomorphism between networks and the stability of network invariants"
    - "hierarchical clustering of asymmetric networks"
    - "topological data analysis with bregman divergences"
    - "a cost function for similarity based hierarchical clustering"

Connected through hyperedge HE-7 (induced by source paper "network comparison embeddings and interiors"):
  with members:
    - "metrics in the space of high order networks"
    - "persistent homology lower bounds on high order network distances"

Connected through hyperedge HE-8 (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"
    - "hierarchical quasi clustering methods for asymmetric networks"
    - "detect overlapping and hierarchical community structure in networks"
    - "overlapping multihop clustering for wireless sensor 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.LG(Machine Learning)