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: "experimental evaluation of counting subgraph isomorphisms in classes of bounded expansion"
  Abstract: Counting subgraph isomorphisms (also called motifs or graphlets) has been used extensively as a tool for analyzing biological and social networks. Under standard complexity assumptions there is no polynomial time algorithm for this problem, which limits the applicability of these tools to large data sets. Recent techniques from parameterized complexity have led to an algorithmic framework for isomorphism counting whose worst case time complexity is linear in the number of vertices, provided that the input graph has certain structural characteristics, known as bounded expansion. Previous work has suggested that the restrictions of bounded expansion structure--locally dense pockets in a globally sparse graph--naturally coincide with common properties of real-world networks such as clustering and heavy-tailed degree distributions. However, there has been little work done in implementing and evaluating the performance of this algorithmic pipeline. To this end we introduced CONCUSS, an open-source software package for counting subgraph isomorphisms in classes of bounded expansion. Through a broad set of experiments we evaluate implementations of multiple stages of the pipeline and demonstrate that our structure-based algorithm can be up to an order of magnitude faster than a popular algorithm for isomorphism counting.

Connected through hyperedge HE-1 (induced by source paper "a color avoiding approach to subgraph counting in bounded expansion classes"):
  with members:
    - "empirical evaluation of approximation algorithms for generalized graph coloring and uniform quasi wideness"
    - "structural sparsity of complex networks bounded expansion in random models and real world graphs"
    - "a dynamic data structure for counting subgraphs in sparse graphs"
    - "listing all maximal cliques in sparse graphs in near optimal time"

Connected through hyperedge HE-2 (induced by source paper "empirical evaluation of approximation algorithms for generalized graph coloring and uniform quasi wideness"):
  with members:
    - "exactly solving the maximum weight independent set problem on large real world graphs"
    - "experimental evaluation of kernelization algorithms to dominating set"
    - "polynomial bounds for centered colorings on proper minor closed graph classes"
    - "kernelization and approximation of distance r independent sets on nowhere dense graphs"
    - "distributed domination on graph classes of bounded expansion"
    - "reconfiguration on nowhere dense graph classes"
    - "positive instance driven dynamic programming for treewidth"
    - "on the number of types in sparse graphs"

Connected through hyperedge HE-3 (induced by source paper "polynomial bounds for centered colorings on proper minor closed graph classes"):
  with members:
    - "on space efficiency of algorithms working on structural decompositions of graphs"
    - "planar subgraph isomorphism revisited"
    - "optimally cutting a surface into a disk"
    - "subgraph isomorphism in planar graphs and related problems"

Connected through hyperedge HE-4 (induced by source paper "polynomial treedepth bounds in linear colorings"):
  with members:
    - "a faster parameterized algorithm for treedepth"

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.DS(Data Structures and Algorithms)