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: "graphs identified by logics with counting"
  Abstract: We classify graphs and, more generally, finite relational structures that are identified by C2, that is, two-variable first-order logic with counting. Using this classification, we show that it can be decided in almost linear time whether a structure is identified by C2. Our classification implies that for every graph identified by this logic, all vertex-colored versions of it are also identified. A similar statement is true for finite relational structures. #R##N#We provide constructions that solve the inversion problem for finite structures in linear time. This problem has previously been shown to be polynomial time solvable by Martin Otto. For graphs, we conclude that every C2-equivalence class contains a graph whose orbits are exactly the classes of the C2-partition of its vertex set and which has a single automorphism witnessing this fact. #R##N#For general k, we show that such statements are not true by providing examples of graphs of size linear in k which are identified by C3 but for which the orbit partition is strictly finer than the Ck-partition. We also provide identified graphs which have vertex-colored versions that are not identified by Ck.

Connected through hyperedge HE-1 (induced by source paper "what graph neural networks cannot learn depth vs width"):
  with members:
    - "edgenets edge varying graph neural networks"
    - "universal invariant and equivariant graph neural networks"
    - "lanczosnet multi scale deep graph convolutional networks"
    - "mincut pooling in graph neural networks"
    - "break the ceiling stronger multi scale deep graph convolutional networks"
    - "understanding the representation power of graph neural networks in learning graph topology"
    - "on the universality of invariant networks"
    - "approximation ratios of graph neural networks for combinatorial problems"

Connected through hyperedge HE-2 (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-3 (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"
    - "tight lower and upper bounds for the complexity of canonical colour refinement"
    - "practical graph isomorphism ii"
    - "dimension reduction via colour refinement"

Connected through hyperedge HE-4 (induced by source paper "identifiability of graphs with small color classes by the weisfeiler leman algorithm"):
  with members:
    - "constructing hard examples for graph isomorphism"
    - "benchmark graphs for practical graph isomorphism"
    - "on the combinatorial power of the weisfeiler lehman algorithm"
    - "graph isomorphism in quasipolynomial time"
    - "powers of tensors and fast matrix multiplication"
    - "fast matrix rank algorithms and applications"

Connected through hyperedge HE-5 (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"

Connected through hyperedge HE-6 (induced by source paper "on weisfeiler leman invariance subgraph counts and related graph properties"):
  with members:
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "homomorphisms are a good basis for counting small subgraphs"
    - "on the combinatorial power of the weisfeiler lehman algorithm"
    - "graph isomorphism in quasipolynomial time"
    - "practical graph isomorphism ii"
    - "subgraph frequencies mapping the empirical and extremal geography of large graph collections"

Connected through hyperedge HE-7 (induced by source paper "the weisfeiler leman dimension of planar graphs is at most 3"):
  with members:
    - "near optimal lower bounds on quantifier depth and weisfeiler leman refinement steps"
    - "upper bounds on the quantifier depth for graph differentiation in first order logic"
    - "tangled up in blue a survey on connectivity decompositions and tangles"
    - "dynamic complexity of planar 3 connected graph isomorphism"
    - "practical graph isomorphism ii"
    - "pebble games and linear equations"
    - "planar graphs logical complexity and parallel isomorphism tests"
    - "testing graph isomorphism in parallel by playing a game"

Connected through hyperedge HE-8 (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"
    - "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"
    - "dimension reduction via colour refinement"
    - "from invariants to canonization in parallel"

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)