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 "unsupervised hierarchical graph representation learning by mutual information maximization"):
  Abstract: Graph representation learning based on graph neural networks (GNNs) can greatly improve the performance of downstream tasks, such as node and graph classification. However, the general GNN models cannot aggregate node information in a hierarchical manner, and thus cannot effectively capture the structural features of graphs. In addition, most of the existing hierarchical graph representation learning are supervised, which are limited by the extreme cost of acquiring labeled data. To address these issues, we present an unsupervised graph representation learning method, Unsupervised Hierarchical Graph Representation UHGR, which can generate hierarchical representations of graphs. This contrastive learning technique focuses on maximizing mutual information between "local" and high-level "global" representations, which enables us to learn the node embeddings and graph embeddings without any labeled data. To demonstrate the effectiveness of the proposed method, we perform the node and graph classification using the learned node and graph embeddings. The results show that the proposed method achieves comparable results to state-of-the-art supervised methods on several benchmarks. In addition, our visualization of hierarchical representations indicates that our method can capture meaningful and interpretable clusters.

Includes members:
  Member "fobe and hobe first and high order bipartite embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "partition hypergraphs with embeddings")
  Member "predict then propagate graph neural networks meet personalized pagerank":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bridging the gap between spatial and spectral domains a survey on graph neural networks")
  Member "large scale learnable graph convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "transferring robustness for graph neural network against poisoning attacks")
  Member "janossy pooling learning deep permutation invariant functions for variable size inputs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on the expressive power of graph neural networks")
  Member "harp hierarchical representation learning for networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "better understanding hierarchical visual relationship for image caption")
  Member "neural message passing for quantum chemistry":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "graph2vec learning distributed representations of graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "few shot learning on graphs via super classes based on graph spectral measures")
  Member "predicting organic reaction outcomes with weisfeiler lehman network":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "retrosynthesis prediction with conditional graph logic network")

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)