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 inference learning for semi supervised classification"):
  Abstract: In this work, we address semi-supervised classification of graph data, where the categories of those unlabeled nodes are inferred from labeled nodes as well as graph structures. Recent works often solve this problem via advanced graph convolution in a conventionally supervised manner, but the performance could degrade significantly when labeled data is scarce. To this end, we propose a Graph Inference Learning (GIL) framework to boost the performance of semi-supervised node classification by learning the inference of node labels on graph topology. To bridge the connection between two nodes, we formally define a structure relation by encapsulating node attributes, between-node paths, and local topological structures together, which can make the inference conveniently deduced from one node to another node. For learning the inference process, we further introduce meta-optimization on structure relations from training nodes to validation nodes, such that the learnt graph inference capability can be better self-adapted to testing nodes. Comprehensive evaluations on four benchmark datasets (including Cora, Citeseer, Pubmed, and NELL) demonstrate the superiority of our proposed GIL when compared against state-of-the-art methods on the semi-supervised node classification task.

Includes members:
  Member "gaussian induced convolution for graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "segmented graph bert for graph instance modeling")
  Member "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "physical virtual collaboration graph network for station level metro ridership prediction")
  Member "n gcn multi scale graph convolution for semi supervised node classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fast haar transforms for graph neural networks")
  Member "adversarially regularized graph autoencoder for graph embedding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fastgae fast scalable and effective graph autoencoders with stochastic subgraph decoding")
  Member "topology adaptive graph convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "std net structure preserving and topology adaptive deformation network for 3d reconstruction from a single image")
  Member "learning to learn meta critic networks for sample efficient learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "transferring robustness for graph neural network against poisoning attacks")
  Member "shift aggregate extract networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "rep the set neural networks for learning set representations")
  Member "scene graph generation by iterative message passing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")

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)