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: "a dynamic data structure for counting subgraphs in sparse graphs"
  Abstract: We present a dynamic data structure representing a graph G, which allows addition and removal of edges from G and can determine the number of appearances of a graph of a bounded size as an induced subgraph of G. The queries are answered in constant time. When the data structure is used to represent graphs from a class with bounded expansion (which includes planar graphs and more generally all proper classes closed on topological minors, as well as many other natural classes of graphs with bounded average degree), the amortized time complexity of updates is polylogarithmic.

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"
    - "experimental evaluation of counting subgraph isomorphisms in classes of bounded expansion"
    - "structural sparsity of complex networks bounded expansion in random models and real world graphs"
    - "listing all maximal cliques in sparse graphs in near optimal time"

Connected through hyperedge HE-2 (induced by source paper "improved dynamic graph coloring"):
  with members:
    - "fully dynamic mis in uniformly sparse graphs"
    - "fully dynamic maximal independent set with sublinear update time"
    - "testing bounded arboricity"
    - "fully dynamic almost maximal matching breaking the polynomial barrier for worst case time bounds"
    - "the shortest augmenting paths for online matchings on trees"
    - "dynamic algorithms for graph coloring"
    - "dynamic graph coloring"
    - "dynamic minimum spanning forest with subpolynomial worst case update time"

Connected through hyperedge HE-3 (induced by source paper "dynamic parameterized problems and algorithms"):
  with members:
    - "fully dynamic maximal matching in constant update time"
    - "fully dynamic connectivity in o log n log log n 2 amortized expected time"
    - "linear time kernelization for feedback vertex set"
    - "a linear time parameterized algorithm for directed feedback vertex set"
    - "unifying and strengthening hardness for dynamic problems via the online matrix vector multiplication conjecture"
    - "dynamic graph connectivity with improved worst case update time and sublinear space"
    - "improved algorithms for decremental single source reachability on directed graphs"
    - "fast dynamic graph algorithms for parameterized problems"

Connected through hyperedge HE-4 (induced by source paper "fast dynamic graph algorithms for parameterized problems"):
  with members:
    - "on feedback vertex set new measure and new structures"
    - "dynamic data structure for tree depth decomposition"
    - "faster deterministic fully dynamic graph connectivity"

Connected through hyperedge HE-5 (induced by source paper "orienting fully dynamic graphs with worst case time bounds"):
  with members:
    - "simple deterministic algorithms for fully dynamic maximal matching"
    - "maintaining approximate maximum weighted matching in fully dynamic graphs"
    - "an efficient polynomial time approximation scheme for steiner forest in planar graphs"
    - "all maximal independent sets and dynamic dominance for sparse graphs"

Connected through hyperedge HE-6 (induced by source paper "dynamic data structure for tree depth decomposition"):
  with members:
    - "model checking lower bounds for simple graphs"

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)