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: "dimension reduction via colour refinement"
  Abstract: Colour refinement is a basic algorithmic routine for graph isomorphism testing, appearing as a subroutine in almost all practical isomorphism solvers. It partitions the vertices of a graph into "colour classes" in such a way that all vertices in the same colour class have the same number of neighbours in every colour class. Tinhofer (Disc. App. Math., 1991), Ramana, Scheinerman, and Ullman (Disc. Math., 1994) and Godsil (Lin. Alg. and its App., 1997) established a tight correspondence between colour refinement and fractional isomorphisms of graphs, which are solutions to the LP relaxation of a natural ILP formulation of graph isomorphism. #R##N#We introduce a version of colour refinement for matrices and extend existing quasilinear algorithms for computing the colour classes. Then we generalise the correspondence between colour refinement and fractional automorphisms and develop a theory of fractional automorphisms and isomorphisms of matrices. #R##N#We apply our results to reduce the dimensions of systems of linear equations and linear programs. Specifically, we show that any given LP L can efficiently be transformed into a (potentially) smaller LP L' whose number of variables and constraints is the number of colour classes of the colour refinement algorithm, applied to a matrix associated with the LP. The transformation is such that we can easily (by a linear mapping) map both feasible and optimal solutions back and forth between the two LPs. We demonstrate empirically that colour refinement can indeed greatly reduce the cost of solving linear programs.

Connected through hyperedge HE-1 (induced by source paper "towards a practical k dimensional weisfeiler leman algorithm"):
  with members:
    - "a survey on graph kernels"
    - "provably powerful graph networks"
    - "upper bounds on the quantifier depth for graph differentiation in first order logic"
    - "faster kernels for graphs with continuous attributes via hashing"
    - "graph isomorphism in quasipolynomial time"
    - "graphs identified by logics with counting"
    - "tight lower and upper bounds for the complexity of canonical colour refinement"
    - "practical graph isomorphism ii"

Connected through hyperedge HE-2 (induced by source paper "on the weisfeiler leman dimension of fractional packing"):
  with members:
    - "the power of the weisfeiler leman algorithm to decompose graphs"
    - "a survey on graph kernels"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "definable inapproximability new challenges for duplicator"
    - "graph isomorphism in quasipolynomial time"

Connected through hyperedge HE-3 (induced by source paper "on the expressive power of linear algebra on graphs"):
  with members:
    - "on the expressiveness of lara a unified language for linear and relational algebra"
    - "laradb a minimalist kernel for linear and relational algebra computation"
    - "on the expressive power of query languages for matrices"
    - "on the combinatorial power of the weisfeiler lehman algorithm"
    - "towards linear algebra over normalized data"
    - "descriptive complexity of graph spectra"
    - "foundations of modern query languages for graph databases"
    - "rank logic is dead long live rank logic"

Connected through hyperedge HE-4 (induced by source paper "upper bounds on the quantifier depth for graph differentiation in first order logic"):
  with members:
    - "near optimal lower bounds on quantifier depth and weisfeiler leman refinement steps"
    - "graph isomorphism in quasipolynomial time"
    - "graphs identified by logics with counting"
    - "tight lower and upper bounds for the complexity of canonical colour refinement"
    - "universal covers color refinement and two variable counting logic lower bounds for the depth"
    - "the propagation depth of local consistency"
    - "practical graph isomorphism ii"
    - "from invariants to canonization in parallel"

Connected through hyperedge HE-5 (induced by source paper "graph isomorphism testing boosted by path coloring"):
  with members:
    - "graph isomorphism color refinement and compactness"

Connected through hyperedge HE-6 (induced by source paper "graph isomorphism color refinement and compactness"):
  with members:
    - "graphs identified by logics with counting"
    - "tight lower and upper bounds for the complexity of canonical colour refinement"
    - "pebble games and linear equations"

Connected through hyperedge HE-7 (induced by source paper "coloring large complex networks"):
  with members:
    - "hop doubling label indexing for point to point distance querying on scale free networks"
    - "graph sample and hold a framework for big graph analytics"
    - "estimating clique composition and size distributions from sampled network data"
    - "graph cluster randomization network exposure to multiple universes"
    - "subgraph frequencies mapping the empirical and extremal geography of large graph collections"
    - "network sampling from static to streaming graphs"
    - "exact algorithms for maximum clique a computational study"
    - "exact algorithms for maximum clique a computational study"

Connected through hyperedge HE-8 (induced by source paper "relational linear programs"):
  with members:
    - "tight lower and upper bounds for the complexity of canonical colour refinement"
    - "rockit exploiting parallelism and symmetry for map inference in statistical relational models"
    - "automorphism groups of graphical models and lifted variational inference"
    - "first order mixed integer linear programming"
    - "counting belief propagation"
    - "tuffy scaling up statistical inference in markov logic networks using an rdbms"

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)