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 representation learning via graphical mutual information maximization"):
  Abstract: The richness in the content of various information networks such as social networks and communication networks provides the unprecedented potential for learning high-quality expressive representations without external supervision. This paper investigates how to preserve and extract the abundant information from graph-structured data into embedding space in an unsupervised manner. To this end, we propose a novel concept, Graphical Mutual Information (GMI), to measure the correlation between input graphs and high-level hidden representations. GMI generalizes the idea of conventional mutual information computations from vector space to the graph domain where measuring mutual information from two aspects of node features and topological structure is indispensable. GMI exhibits several benefits: First, it is invariant to the isomorphic transformation of input graphs---an inevitable constraint in many existing graph representation learning algorithms; Besides, it can be efficiently estimated and maximized by current mutual information estimation methods such as MINE; Finally, our theoretical analysis confirms its correctness and rationality. With the aid of GMI, we develop an unsupervised learning model trained by maximizing GMI between the input and output of a graph neural encoder. Considerable experiments on transductive as well as inductive node classification and link prediction demonstrate that our method outperforms state-of-the-art unsupervised counterparts, and even sometimes exceeds the performance of supervised ones.

Includes members:
  Member "gmnn graph markov neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "self supervised graph representation learning via global context prediction")
  Member "a two step graph convolutional decoder for molecule generation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "benchmarking graph neural networks")
  Member "graph wavelet neural network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "spectral graph attention network")
  Member "relation aware entity alignment for heterogeneous knowledge graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep graph matching consensus")
  Member "demo net degree specific graph neural networks for node and graph classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improving node classification by co training node pair classification a novel training framework for general graph neural networks")
  Member "link prediction based on graph neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "can graph neural networks count substructures")
  Member "network embedding as matrix factorization unifying deepwalk line pte and node2vec":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adversarial attacks and defenses on graphs a review and empirical study")
  Member "regal representation learning based graph alignment":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep graph matching consensus")

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)