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: "ricci ollivier curvature of the rooted phylogenetic subtree prune regraft graph"
  Abstract: Statistical phylogenetic inference methods use tree rearrangement operations to perform either hill-climbing local search or Markov chain Monte Carlo across tree topologies. The canonical class of such moves are the subtree-prune-regraft (SPR) moves that remove a subtree and reattach it somewhere else via the cut edge of the subtree. Phylogenetic trees and such moves naturally form the vertices and edges of a graph, such that tree search algorithms perform a (potentially stochastic) traversal of this SPR graph. Despite the centrality of such graphs in phylogenetic inference, rather little is known about their large-scale properties. In this paper we learn about the rooted-tree version of the graph, known as the rSPR graph, by calculating the Ricci-Ollivier curvature for pairs of vertices in the rSPR graph with respect to two simple random walks on the rSPR graph. By proving theorems and direct calculation with novel algorithms, we find a remarkable diversity of different curvatures on the rSPR graph for pairs of vertices separated by the same distance. We confirm using simulation that degree and curvature have the expected impact on mean access time distributions, demonstrating relevance of these curvature results to stochastic tree search. This indicates significant structure of the rSPR graph beyond that which was previously understood in terms of pairwise distances and vertex degrees; a greater understanding of curvature could ultimately lead to improved strategies for tree search.

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"
    - "characterizing complex networks with forman ricci curvature and associated geometric flows"
    - "equivalence between modularity optimization and maximum likelihood methods for community detection"
    - "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 "network alignment by discrete ollivier ricci flow"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "graph isomorphism in quasipolynomial time"
    - "ricci curvature of the internet topology"
    - "on the reliability of profile matching across large online social networks"
    - "deepwalk online learning of social representations"
    - "practical graph isomorphism ii"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-3 (induced by source paper "efficiently inferring pairwise subtree prune and regraft adjacencies between phylogenetic trees"):
  with members:
    - "calculating the unrooted subtree prune and regraft distance"

Connected through hyperedge HE-4 (induced by source paper "chain reduction preserves the unrooted subtree prune and regraft distance"):
  with members:
    - "fixed parameter and approximation algorithms for maximum agreement forests of multifurcating trees"

Connected through hyperedge HE-5 (induced by source paper "calculating the unrooted subtree prune and regraft distance"):
  with members:
    - "chain reduction preserves the unrooted subtree prune and regraft distance"
    - "fixed parameter and approximation algorithms for maximum agreement forests of multifurcating trees"

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)