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 weisfeiler leman invariance subgraph counts and related graph properties"
  Abstract: We investigate graph properties and parameters that are invariant under Weisfeiler-Leman equivalence, focussing especially on 1-dimensional Weisfeiler-Leman equivalence and on subgraph counts that are preserved. Our main results are summarized below: #R##N#-- For 1-dimensional Weisfeiler-Leman equivalence, we completely characterize graphs whose: (a) counts are determined by 1-dimensional Weisfeiler-Leman equivalence, and (b) whose presence is determined by 1-dimensional Weisfeiler-Leman equivalence. #R##N#-- Extending an old result due to Beezer and Farrell for distance-regular graphs, we show that 2-dimensional Weisfeiler-Leman equivalence preserves the first five coefficients of the matching polynomial (and no more). We determine exactly which paths and cycles have their counts determined by 2-dimensional WL-equivalence. #R##N#-- We also study a notion of "approximate invariance" of graph parameters under Weisfeiler-Leman equivalence. We show that the fractional matching number and fractional domination number are preserved by 1-dimensional Weisfeiler-Leman equivalence.

Connected through hyperedge HE-1 (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-2 (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"
    - "homomorphisms are a good basis for counting small subgraphs"
    - "benchmark graphs for practical graph isomorphism"
    - "on the combinatorial power of the weisfeiler lehman algorithm"
    - "graph isomorphism in quasipolynomial time"

Connected through hyperedge HE-3 (induced by source paper "symmetric arithmetic circuits"):
  with members:
    - "on the power of symmetric linear programs"
    - "symmetric circuits for rank logic"
    - "functional lower bounds for arithmetic circuits and connections to boolean circuit complexity"
    - "on symmetric circuits and fixed point logics"

Connected through hyperedge HE-4 (induced by source paper "incorporating weisfeiler leman into algorithms for group isomorphism"):
  with members:
    - "on the group and color isomorphism problems"
    - "graph isomorphism in quasipolynomial time"
    - "bidirectional collision detection and faster deterministic isomorphism testing"
    - "beating the generator enumeration bound for p group isomorphism"

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.DM(Discrete Mathematics)