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: "on the combinatorial power of the weisfeiler lehman algorithm"
  Abstract: The classical Weisfeiler-Lehman method WL[2] uses edge colors to produce a powerful graph invariant. It is at least as powerful in its ability to distinguish non-isomorphic graphs as the most prominent algebraic graph invariants. It determines not only the spectrum of a graph, and the angles between standard basis vectors and the eigenspaces, but even the angles between projections of standard basis vectors into the eigenspaces. Here, we investigate the combinatorial power of WL[2]. For sufficiently large k, WL[k] determines all combinatorial properties of a graph. Many traditionally used combinatorial invariants are determined by WL[k] for small k. We focus on two fundamental invariants, the num- ber of cycles Cp of length p, and the number of cliques Kp of size p. We show that WL[2] determines the number of cycles of lengths up to 6, but not those of length 8. Also, WL[2] does not determine the number of 4-cliques.

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 "can graph neural networks count substructures"):
  with members:
    - "composing molecules with multiple property constraints"
    - "generalization and representational limits of graph neural networks"
    - "universal invariant and equivariant graph neural networks"
    - "g2sat learning to generate sat formulas"
    - "cognitive graph for multi hop reading comprehension at scale"
    - "on the universality of invariant networks"
    - "what graph neural networks cannot learn depth vs width"
    - "gnn explainer a tool for post hoc explanation of graph neural networks"

Connected through hyperedge HE-3 (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"
    - "graph isomorphism in quasipolynomial time"

Connected through hyperedge HE-4 (induced by source paper "on the equivalence between node embeddings and structural graph representations"):
  with members:
    - "from community to role based graph embeddings"
    - "graph representation ensemble learning"
    - "is a single embedding enough learning node representations that capture multiple social contexts"
    - "on the limitations of representing functions on sets"
    - "position aware graph neural networks"
    - "what can neural networks reason about"
    - "correlated variational auto encoders"
    - "on the equivalence between graph isomorphism testing and function approximation with gnns"

Connected through hyperedge HE-5 (induced by source paper "relational pooling for graph representations"):
  with members:
    - "on the universality of invariant networks"
    - "pitfalls of graph neural network evaluation"
    - "hierarchical graph representation learning with differentiable pooling"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "representation learning on graphs with jumping knowledge networks"
    - "deep sets"
    - "relational inductive biases deep learning and graph networks"
    - "invariant and equivariant graph networks"

Connected through hyperedge HE-6 (induced by source paper "a hierarchy of graph neural networks based on learnable local features"):
  with members:
    - "on the limitations of representing functions on sets"
    - "simplifying graph convolutional networks"
    - "a comprehensive survey on graph neural networks"
    - "graph neural tangent kernel fusing graph neural networks with graph kernels"
    - "graph neural networks a review of methods and applications"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "n gcn multi scale graph convolution for semi supervised node classification"

Connected through hyperedge HE-7 (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"

Connected through hyperedge HE-8 (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"
    - "semi supervised classification with graph convolutional networks"
    - "upper bounds on the quantifier depth for graph differentiation in first order logic"
    - "graph isomorphism in quasipolynomial time"

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)