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 "gaussian induced convolution for graphs"):
  Abstract: Learning representation on graph plays a crucial role in numerous tasks of pattern recognition. Different from grid-shaped images/videos, on which local convolution kernels can be lattices, however, graphs are fully coordinate-free on vertices and edges. In this work, we propose a Gaussian-induced convolution (GIC) framework to conduct local convolution filtering on irregular graphs. Specifically, an edge-induced Gaussian mixture model is designed to encode variations of subgraph region by integrating edge information into weighted Gaussian models, each of which implicitly characterizes one component of subgraph variations. In order to coarsen a graph, we derive a vertex-induced Gaussian mixture model to cluster vertices dynamically according to the connection of edges, which is approximately equivalent to the weighted graph cut. We conduct our multi-layer graph convolution network on several public datasets of graph classification. The extensive experiments demonstrate that our GIC is effective and can achieve the state-of-the-art results.

Includes members:
  Member "context dependent diffusion network for visual relationship detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "visual relationship detection with relative location mining")
  Member "tensor graph convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "graph prolongation convolutional networks explicitly multiscale machine learning on graphs with applications to modeling of biological systems")
  Member "action attending graphic neural network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "human action performance using deep neuro fuzzy recurrent attention model")
  Member "when work matters transforming classical network structures to graph cnn":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "context dependent diffusion network for visual relationship detection")
  Member "walk steered convolution for graph classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "context dependent diffusion network for visual relationship detection")
  Member "spatio temporal graph convolution for skeleton based action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "predictively encoded graph convolutional network for noise robust skeleton based action recognition")
  Member "shift aggregate extract networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "graph inference learning for semi supervised classification")
  Member "spectral filter tracking":
    also connected through other hyperedges:
      (none)

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)