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: "metrics in the space of high order networks"
  Abstract: This paper presents methods to compare high-order networks, defined as weighted complete hypergraphs collecting relationship functions between elements of tuples. They can be considered as generalizations of conventional networks where only relationship functions between pairs are defined. Important properties between relationships of tuples of different lengths are established, particularly when relationships encode dissimilarities or proximities between nodes. Two families of distances are then introduced in the space of high-order networks. The distances measure differences between networks. We prove that they are valid metrics in the spaces of high-order dissimilarity and proximity networks modulo permutation isomorphisms. Practical implications are explored by comparing the coauthorship networks of two popular signal processing researchers. The metrics succeed in identifying their respective collaboration patterns.

Connected through hyperedge HE-1 (induced by source paper "network comparison embeddings and interiors"):
  with members:
    - "persistent homology lower bounds on high order network distances"
    - "axiomatic construction of hierarchical clustering in asymmetric networks"

Connected through hyperedge HE-2 (induced by source paper "hierarchical clustering given confidence intervals of metric distances"):
  with members:
    - "persistent homology lower bounds on high order network distances"
    - "axiomatic construction of hierarchical clustering in asymmetric networks"
    - "a uniqueness theorem for clustering"

Connected through hyperedge HE-3 (induced by source paper "persistent homology lower bounds on high order network distances"):
  with members:
    - "hierarchical clustering given confidence intervals of metric distances"
    - "the theory of the interleaving distance on multidimensional persistence modules"
    - "axiomatic construction of hierarchical clustering in asymmetric 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)