human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "towards a practical k dimensional weisfeiler leman algorithm"):
  Abstract: The $k$-dimensional Weisfeiler-Leman algorithm is a well-known heuristic for the graph isomorphism problem. Moreover, it recently emerged as a powerful tool for supervised graph classification. The algorithm iteratively partitions the set of $k$-tuples, defined over the set of vertices of a graph, by considering neighboring $k$-tuples. Here, we propose a \new{local} variant which considers a subset of the original neighborhood in each iteration step. The cardinality of this local neighborhood, unlike the original one, only depends on the sparsity of the graph. Surprisingly, we show that the local variant has at least the same power as the original algorithm in terms of distinguishing non-isomorphic graphs. In order to demonstrate the practical utility of our local variant, we apply it to supervised graph classification. Our experimental study shows that our local algorithm leads to improved running times and classification accuracies on established benchmark datasets.

Includes members:
  Member "a survey on graph kernels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "generalization and representational limits of graph neural networks")
  Member "provably powerful graph networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on the expressive power of graph neural networks")
  Member "upper bounds on the quantifier depth for graph differentiation in first order logic":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a linear upper bound on the weisfeiler leman dimension of graphs of bounded genus")
  Member "faster kernels for graphs with continuous attributes via hashing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "understanding isomorphism bias in graph data sets")
  Member "graph isomorphism in quasipolynomial time":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hypergraph isomorphism for groups with restricted composition factors")
  Member "graphs identified by logics with counting":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "what graph neural networks cannot learn depth vs width")
  Member "tight lower and upper bounds for the complexity of canonical colour refinement":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hypergraph isomorphism for groups with restricted composition factors")
  Member "practical graph isomorphism ii":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "hypergraph isomorphism for groups with restricted composition factors")

Question: Please tell me which class the source paper 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)