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: "tight lower and upper bounds for the complexity of canonical colour refinement"
  Abstract: An assignment of colours to the vertices of a graph is stable if any two vertices of the same colour have identically coloured neighbourhoods. The goal of colour refinement is to find a stable colouring that uses a minimum number of colours. This is a widely used subroutine for graph isomorphism testing algorithms, since any automorphism needs to be colour preserving. We give an $O((m+n)\log n)$ algorithm for finding a canonical version of such a stable colouring, on graphs with $n$ vertices and $m$ edges. We show that no faster algorithm is possible, under some modest assumptions about the type of algorithm, which captures all known colour refinement algorithms.

Connected through hyperedge HE-1 (induced by source paper "hypergraph isomorphism for groups with restricted composition factors"):
  with members:
    - "graph isomorphism in quasipolynomial time parameterized by treewidth"
    - "an improved isomorphism test for bounded tree width graphs"
    - "a faster isomorphism test for graphs of small degree"
    - "a unifying method for the design of algorithms canonizing combinatorial objects"
    - "graph isomorphism for unit square graphs"
    - "graph isomorphism in quasipolynomial time"
    - "graph isomorphism for bounded genus graphs in linear time"
    - "isomorphism testing for graphs of bounded rank width"

Connected through hyperedge HE-2 (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"
    - "practical graph isomorphism ii"
    - "dimension reduction via colour refinement"

Connected through hyperedge HE-3 (induced by source paper "jgrapht a java library for graph data structures and algorithms"):
  with members:
    - "graphblast a high performance linear algebra based graph framework on the gpu"
    - "mathematical foundations of the graphblas"
    - "gunrock a high performance graph processing library on the gpu"
    - "networkit a tool suite for large scale complex network analysis"
    - "pagerank beyond the web"
    - "malware classification based on call graph clustering"
    - "listing all maximal cliques in sparse graphs in near optimal time"

Connected through hyperedge HE-4 (induced by source paper "generic partition refinement and weighted tree automata"):
  with members:
    - "efficient and modular coalgebraic partition refinement"
    - "efficient coalgebraic partition refinement"
    - "automata theory in nominal sets"
    - "generalizing the paige tarjan algorithm by abstract interpretation"

Connected through hyperedge HE-5 (induced by source paper "efficient and modular coalgebraic partition refinement"):
  with members:
    - "generic partition refinement and weighted tree automata"
    - "a new foundation for finitary corecursion and iterative algebras"
    - "efficient coalgebraic partition refinement"
    - "generalizing the paige tarjan algorithm by abstract interpretation"

Connected through hyperedge HE-6 (induced by source paper "global weisfeiler lehman graph kernels"):
  with members:
    - "a general framework for estimating graphlet statistics via random walk"
    - "learning convolutional neural networks for graphs"
    - "subgraph matching kernels for attributed graphs"

Connected through hyperedge HE-7 (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"
    - "universal covers color refinement and two variable counting logic lower bounds for the depth"
    - "the propagation depth of local consistency"
    - "practical graph isomorphism ii"
    - "dimension reduction via colour refinement"
    - "from invariants to canonization in parallel"

Connected through hyperedge HE-8 (induced by source paper "learning convolutional neural networks for graphs"):
  with members:
    - "gated graph sequence neural networks"
    - "deep convolutional networks on graph structured data"
    - "convolutional networks on graphs for learning molecular fingerprints"
    - "atomnet a deep convolutional neural network for bioactivity prediction in structure based drug discovery"
    - "spectral networks and locally connected networks on graphs"
    - "practical graph isomorphism ii"
    - "counting belief propagation"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

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)