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: "distances and isomorphism between networks and the stability of network invariants"
  Abstract: We develop the theoretical foundations of a network distance that has recently been applied to various subfields of topological data analysis, namely persistent homology and hierarchical clustering. While this network distance has previously appeared in the context of finite networks, we extend the setting to that of compact networks. The main challenge in this new setting is the lack of an easy notion of sampling from compact networks; we solve this problem in the process of obtaining our results. The generality of our setting means that we automatically establish results for exotic objects such as directed metric spaces and Finsler manifolds. We identify readily computable network invariants and establish their quantitative stability under this network distance. We also discuss the computational complexity involved in precisely computing this distance, and develop easily-computable lower bounds by using the identified invariants. By constructing a wide range of explicit examples, we show that these lower bounds are effective in distinguishing between networks. Finally, we provide a simple algorithm that computes a lower bound on the distance between two networks in polynomial time and illustrate our metric and invariant constructions on a database of random networks and a database of simulated hippocampal networks.

Connected through hyperedge HE-1 (induced by source paper "motivic clustering schemes for directed graphs"):
  with members:
    - "excisive hierarchical clustering methods for network data"
    - "axiomatic construction of hierarchical clustering in asymmetric networks"

Connected through hyperedge HE-2 (induced by source paper "graph distance from the topological view of non backtracking cycles"):
  with members:
    - "an information theoretic all scales approach to comparing networks"
    - "on spectral graph embedding a non backtracking perspective and graph approximation"
    - "the metric space of networks"
    - "localization and centrality in networks"
    - "an in depth analysis of stochastic kronecker graphs"
    - "deltacon a principled massive graph similarity function"
    - "spectral redemption in clustering sparse networks"
    - "network sampling from static to streaming graphs"

Connected through hyperedge HE-3 (induced by source paper "the metric space of networks"):
  with members:
    - "hierarchical quasi clustering methods for asymmetric networks"
    - "axiomatic construction of hierarchical clustering in asymmetric networks"

Connected through hyperedge HE-4 (induced by source paper "convergence of hierarchical clustering and persistent homology methods on directed networks"):
  with members:
    - "hierarchical clustering of asymmetric networks"
    - "topological data analysis with bregman divergences"
    - "a cost function for similarity based hierarchical clustering"
    - "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.DM(Discrete Mathematics)