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: "pebble games and linear equations"
  Abstract: We give a new, simplified and detailed account of the correspondence between levels of the Sherali-Adams relaxation of graph isomorphism and levels of pebble-game equivalence with counting (higher-dimensional Weisfeiler-Lehman colour refinement). The correspondence between basic colour refinement and fractional isomorphism, due to Tinhofer (1986, 1991) and Ramana, Scheinerman and Ullman (1994), is re-interpreted as the base level of Sherali-Adams and generalised to higher levels in this sense by Atserias and Maneva (2012) and Malkin (2014), who prove that the two resulting hierarchies interleave. #R##N#In carrying this analysis further, we here give #R##N#(a) a precise characterisation of the level-k Sherali-Adams relaxation in terms of a modified counting pebble game; #R##N#(b) a variant of the Sherali-Adams levels that precisely match the k-pebble counting game; #R##N#(c) a proof that the interleaving between these two hierarchies is strict. #R##N#We also investigate the variation based on boolean arithmetic instead of real/rational arithmetic and obtain analogous correspondences and separations for plain k-pebble equivalence (without counting). Our results are driven by considerably simplified accounts of the underlying combinatorics and linear algebra.

Connected through hyperedge HE-1 (induced by source paper "a survey on the expressive power of graph neural networks"):
  with members:
    - "random features strengthen graph neural networks"
    - "generalization and representational limits of graph neural networks"
    - "universal invariant and equivariant graph neural networks"
    - "exact combinatorial optimization with graph convolutional neural networks"
    - "exact k recommendation via maximal clique optimization"
    - "constant time graph neural networks"
    - "kgat knowledge graph attention network for recommendation"
    - "understanding attention and generalization in graph neural networks"

Connected through hyperedge HE-2 (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"
    - "graph isomorphism in quasipolynomial time"
    - "limitations of algebraic approaches to graph isomorphism testing"
    - "graph isomorphism parameterized by elimination distance to bounded degree"

Connected through hyperedge HE-3 (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-4 (induced by source paper "a linear upper bound on the weisfeiler leman dimension of graphs of bounded genus"):
  with members:
    - "canonisation and definability for graphs of bounded rank width"
    - "a finite model theoretic view on propositional proof complexity"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "definable ellipsoid method sums of squares proofs and the isomorphism problem"
    - "the weisfeiler leman dimension of planar graphs is at most 3"
    - "on the combinatorial power of the weisfeiler lehman algorithm"
    - "semi supervised classification with graph convolutional networks"
    - "upper bounds on the quantifier depth for graph differentiation in first order logic"

Connected through hyperedge HE-5 (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-6 (induced by source paper "the power of the weisfeiler leman algorithm to decompose graphs"):
  with members:
    - "towards a practical k dimensional weisfeiler leman algorithm"
    - "canonisation and definability for graphs of bounded rank width"
    - "a linear upper bound on the weisfeiler leman dimension of graphs of bounded genus"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "lov asz meets weisfeiler and leman"
    - "the weisfeiler leman dimension of planar graphs is at most 3"
    - "on the combinatorial power of the weisfeiler lehman algorithm"
    - "graph isomorphism for unit square graphs"

Connected through hyperedge HE-7 (induced by source paper "approximations of isomorphism and logics with linear algebraic operators"):
  with members:
    - "a finite model theoretic view on propositional proof complexity"
    - "graph isomorphism in quasipolynomial time"
    - "rank logic is dead long live rank logic"
    - "definability of linear equation systems over groups and rings"
    - "pebble games with algebraic rules"

Connected through hyperedge HE-8 (induced by source paper "weisfeiler and leman go neural higher order graph neural networks"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "hierarchical graph representation learning with differentiable pooling"
    - "representation learning on graphs with jumping knowledge networks"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "dynamic graph cnn for learning on point clouds"
    - "deriving neural architectures from sequence and graph kernels"
    - "splinecnn fast geometric deep learning with continuous b spline kernels"
    - "representation learning on graphs methods and applications"

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.LO(Logic in Computer Science)