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: "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.

Connected through hyperedge HE-1 (induced by source paper "graph inference learning for semi supervised classification"):
  with members:
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "n gcn multi scale graph convolution for semi supervised node classification"
    - "adversarially regularized graph autoencoder for graph embedding"
    - "topology adaptive graph convolutional networks"
    - "learning to learn meta critic networks for sample efficient learning"
    - "shift aggregate extract networks"
    - "scene graph generation by iterative message passing"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-2 (induced by source paper "segmented graph bert for graph instance modeling"):
  with members:
    - "graph bert only attention is needed for learning graph representations"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "dagcn dual attention graph convolutional networks"
    - "asap adaptive structure aware pooling for learning hierarchical graph representations"
    - "isonn isomorphic neural network for graph representation learning and classification"
    - "capsule neural networks for graph classification using explicit tensorial graph representations"
    - "gresnet graph residual network for reviving deep gnns from suspended animation"
    - "graph neural networks for small graph and giant network representation learning an overview"

Connected through hyperedge HE-3 (induced by source paper "walk steered convolution for graph classification"):
  with members:
    - "graph embedding techniques applications and performance a survey"
    - "temporal segment networks for action recognition in videos"
    - "representation learning on graphs methods and applications"
    - "shift aggregate extract networks"
    - "robust spatial filtering with graph convolutional neural networks"
    - "learning graph representations with embedding propagation"
    - "gated graph sequence neural networks"
    - "node2vec scalable feature learning for networks"

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.LG(Machine Learning)