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 "deep graph mapper seeing graphs through the neural lens"):
  Abstract: Recent advancements in graph representation learning have led to the emergence of condensed encodings that capture the main properties of a graph. However, even though these abstract representations are powerful for downstream tasks, they are not equally suitable for visualisation purposes. In this work, we merge Mapper, an algorithm from the field of Topological Data Analysis (TDA), with the expressive power of Graph Neural Networks (GNNs) to produce hierarchical, topologically-grounded visualisations of graphs. These visualisations do not only help discern the structure of complex graphs but also provide a means of understanding the models applied to them for solving various tasks. We further demonstrate the suitability of Mapper as a topological framework for graph pooling by mathematically proving an equivalence with Min-Cut and Diff Pool. Building upon this framework, we introduce a novel pooling algorithm based on PageRank, which obtains competitive results with state of the art methods on graph classification benchmarks.

Includes members:
  Member "mincut pooling in graph neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "memory based graph networks")
  Member "asap adaptive structure aware pooling for learning hierarchical graph representations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "segmented graph bert for graph instance modeling")
  Member "graph convolutional networks with eigenpooling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "transferring robustness for graph neural network against poisoning attacks")
  Member "clique pooling for graph classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "memory based graph networks")
  Member "graph u nets":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "memory based graph networks")
  Member "on graph classification networks datasets and baselines":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "benchmarking graph neural networks")
  Member "self attention graph pooling":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "memory based graph networks")
  Member "graph filtration learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "universal function approximation on graphs using multivalued functions")

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.LG(Machine Learning)