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: "characterizing complex networks with forman ricci curvature and associated geometric flows"
  Abstract: We introduce Forman-Ricci curvature and its corresponding flow as characteristics for complex networks attempting to extend the common approach of node-based network analysis by edge-based characteristics. Following a theoretical introduction and mathematical motivation, we apply the proposed network-analytic methods to static and dynamic complex networks and compare the results with established node-based characteristics. Our work suggests a number of applications for data mining, including denoising and clustering of experimental data, as well as extrapolation of network evolution.

Connected through hyperedge HE-1 (induced by source paper "community detection on networks with ricci flow"):
  with members:
    - "network alignment by discrete ollivier ricci flow"
    - "the ground truth about metadata and community detection in networks"
    - "equivalence between modularity optimization and maximum likelihood methods for community detection"
    - "ricci ollivier curvature of the rooted phylogenetic subtree prune regraft graph"
    - "ricci curvature of the internet topology"
    - "empirical comparison of algorithms for network community detection"
    - "objective criteria for the evaluation of clustering methods"

Connected through hyperedge HE-2 (induced by source paper "measuring road network topology vulnerability by ricci curvature"):
  with members:
    - "discriminative distance based network indices with application to link prediction"
    - "ricci curvature of the internet topology"

Connected through hyperedge HE-3 (induced by source paper "forman s ricci curvature from networks to hypernetworks"):
  with members:
    - "curvature of hypergraphs via multi marginal optimal transport"
    - "structural deep embedding for hyper networks"
    - "forman ricci flow for change detection in large dynamic data sets"

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)