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: "graph isomorphism in quasipolynomial time"
  Abstract: We show that the Graph Isomorphism (GI) problem and the related problems of String Isomorphism (under group action) (SI) and Coset Intersection (CI) can be solved in quasipolynomial (exp (logn) O(1) � ) time. The best previous bound for GI was exp(O( √ nlogn)), where n is the number of vertices (Luks, 1983); for the other two problems, the bound was similar, exp( e O( √ n)), where n is the size of the permutation domain (Babai, 1983). The algorithm builds on Luks’s SI framework and attacks the barrier configurations for Luks’s algorithm by group theoretic “local certificates” and combinatorial canonical partitioning techniques. We show that in a well-defined sense, Johnson graphs are the only obstructions to effective canonical partitioning.

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 for bounded genus graphs in linear time"
    - "tight lower and upper bounds for the complexity of canonical colour refinement"
    - "isomorphism testing for graphs of bounded rank width"

Connected through hyperedge HE-2 (induced by source paper "universal function approximation on graphs using multivalued functions"):
  with members:
    - "a survey on graph kernels"
    - "graph filtration learning"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "deep learning on graphs a survey"
    - "how powerful are graph neural networks"
    - "neural network with unbounded activation functions is universal approximator"
    - "understanding deep neural networks with rectified linear units"
    - "diffusion convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "estimating descriptors for large graphs"):
  with members:
    - "fleet butterfly estimation from a bipartite graph stream"
    - "stochastic graphlet embedding"
    - "a comprehensive survey on graph neural networks"
    - "netlsd hearing the shape of a graph"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "how powerful are graph neural networks"
    - "wrs waiting room sampling for accurate triangle counting in real graph streams"
    - "deltacon a principled massive graph similarity function"

Connected through hyperedge HE-4 (induced by source paper "counting bounded tree depth homomorphisms"):
  with members:
    - "a survey on graph kernels"
    - "a simple proof of the universality of invariant equivariant graph neural networks"
    - "a finite model theoretic view on propositional proof complexity"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "limitations of algebraic approaches to graph isomorphism testing"
    - "graph isomorphism parameterized by elimination distance to bounded degree"
    - "pebble games and linear equations"

Connected through hyperedge HE-5 (induced by source paper "local wl invariance and hidden shades of regularity"):
  with members:
    - "identifiability of graphs with small color classes by the weisfeiler leman algorithm"
    - "on the weisfeiler leman dimension of fractional packing"
    - "a survey on graph kernels"
    - "on weisfeiler leman invariance subgraph counts and related graph properties"
    - "homomorphisms are a good basis for counting small subgraphs"
    - "benchmark graphs for practical graph isomorphism"
    - "on the combinatorial power of the weisfeiler lehman algorithm"

Connected through hyperedge HE-6 (induced by source paper "provably powerful graph networks"):
  with members:
    - "universal invariant and equivariant graph neural networks"
    - "towards a practical k dimensional weisfeiler leman algorithm"
    - "on the universality of invariant networks"
    - "a survey on graph kernels"
    - "fast graph representation learning with pytorch geometric"
    - "relational pooling for graph representations"
    - "dual primal graph convolutional networks"
    - "hierarchical graph representation learning with differentiable pooling"

Connected through hyperedge HE-7 (induced by source paper "coloring graph neural networks for node disambiguation"):
  with members:
    - "on the universality of invariant networks"
    - "provably powerful graph networks"
    - "on the equivalence between graph isomorphism testing and function approximation with gnns"
    - "relational pooling for graph representations"
    - "hierarchical graph representation learning with differentiable pooling"
    - "deep sets"
    - "invariant and equivariant graph networks"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-8 (induced by source paper "understanding isomorphism bias in graph data sets"):
  with members:
    - "semantic redundancies in image classification datasets the 10 you don t need"
    - "do we train on test data purging cifar of near duplicates"
    - "a survey on graph kernels"
    - "gnn explainer a tool for post hoc explanation of graph neural networks"
    - "do imagenet classifiers generalize to imagenet"
    - "fast graph representation learning with pytorch geometric"
    - "provably powerful graph networks"
    - "stability and generalization of graph convolutional neural networks"

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)