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 "graph prolongation convolutional networks explicitly multiscale machine learning on graphs with applications to modeling of biological systems"):
  Abstract: We define a novel type of ensemble Graph Convolutional Network (GCN) model. Using optimized linear projection operators to map between spatial scales of graph, this ensemble model learns to aggregate information from each scale for its final prediction. We calculate these linear projection operators as the infima of an objective function relating the structure matrices used for each GCN. Equipped with these projections, our model (a Graph Prolongation-Convolutional Network) outperforms other GCN ensemble models at predicting the potential energy of monomer subunits in a coarse-grained mechanochemical simulation of microtubule bending. We demonstrate these performance gains by measuring an estimate of the FLOPs spent to train each model, as well as wall-clock time. Because our model learns at multiple scales, it is possible to train at each scale according to a predetermined schedule of coarse vs. fine training. We examine several such schedules adapted from the Algebraic Multigrid (AMG) literature, and quantify the computational benefit of each. Finally, we demonstrate how under certain assumptions, our graph prolongation layers may be decomposed into a matrix outer product of smaller GCN operations.

Includes members:
  Member "novel diffusion derived distance measures for graphs":
    also connected through other hyperedges:
      (none)
  Member "lanczosnet multi scale deep graph convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "spectral graph attention network")
  Member "a gentle introduction to deep learning for graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "benchmarking graph neural networks")
  Member "hierarchical graph representation learning with differentiable pooling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "memory based graph networks")
  Member "multilevel artificial neural network training for spatially correlated learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "novel diffusion derived distance measures for graphs")
  Member "tensor graph convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "context dependent diffusion network for visual relationship detection")
  Member "pymanopt a python toolbox for optimization on manifolds using automatic differentiation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "coresets for gaussian mixture models of any shape")
  Member "multigrid neural architectures":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "slimconv reducing channel redundancy in convolutional neural networks by weights flipping")

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)