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 "random features strengthen graph neural networks"):
  Abstract: Graph neural networks (GNNs) are powerful machine learning models for various graph learning tasks. Recently, the limitations of the expressive power of various GNN models have been revealed. For example, GNNs cannot distinguish some non-isomorphic graphs and they cannot learn efficient graph algorithms, and several GNN models have been proposed to overcome these limitations. In this paper, we demonstrate that GNNs become powerful just by adding a random feature to each node. We prove that the random features enable GNNs to learn almost optimal polynomial-time approximation algorithms for the minimum dominating set problem and maximum matching problem in terms of the approximation ratio. The main advantage of our method is that it can be combined with off-the-shelf GNN models with slight modifications. Through experiments, we show that the addition of random features enables GNNs to solve various problems that normal GNNs, including GCNs and GINs, cannot solve.

Includes members:
  Member "universal invariant and equivariant graph neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on the expressive power of graph neural networks")
  Member "exact k recommendation via maximal clique optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on the expressive power of graph neural networks")
  Member "kgat knowledge graph attention network for recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on the expressive power of graph neural networks")
  Member "graph convolutional networks with eigenpooling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "transferring robustness for graph neural network against poisoning attacks")
  Member "graph u nets":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "memory based graph networks")
  Member "on the universality of invariant networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on the expressive power of graph neural networks")
  Member "approximation ratios of graph neural networks for combinatorial problems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on the expressive power of graph neural networks")
  Member "what graph neural networks cannot learn depth vs width":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a survey on the expressive power of graph neural networks")

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)