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 neural distance metric learning with graph bert"):
  Abstract: Graph distance metric learning serves as the foundation for many graph learning problems, e.g., graph clustering, graph classification and graph matching. Existing research works on graph distance metric (or graph kernels) learning fail to maintain the basic properties of such metrics, e.g., non-negative, identity of indiscernibles, symmetry and triangle inequality, respectively. In this paper, we will introduce a new graph neural network based distance metric learning approaches, namely GB-DISTANCE (GRAPH-BERT based Neural Distance). Solely based on the attention mechanism, GB-DISTANCE can learn graph instance representations effectively based on a pre-trained GRAPH-BERT model. Different from the existing supervised/unsupervised metrics, GB-DISTANCE can be learned effectively in a semi-supervised manner. In addition, GB-DISTANCE can also maintain the distance metric basic properties mentioned above. Extensive experiments have been done on several benchmark graph datasets, and the results demonstrate that GB-DISTANCE can out-perform the existing baseline methods, especially the recent graph neural network model based graph metrics, with a significant gap in computing the graph distance.

Includes members:
  Member "simgnn a neural network approach to fast graph similarity computation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep graph matching consensus")
  Member "graph bert only attention is needed for learning graph representations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "segmented graph bert for graph instance modeling")
  Member "adagcn adaboosting graph convolutional networks into deep models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "get rid of suspended animation problem deep diffusive neural network on graph semi supervised classification")
  Member "isonn isomorphic neural network for graph representation learning and classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "segmented graph bert for graph instance modeling")
  Member "structure fusion based on graph convolutional networks for semi supervised classification":
    also connected through other hyperedges:
      (none)
  Member "gresnet graph residual network for reviving deep gnns from suspended animation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ripple walk training a subgraph based training framework for large and deep graph neural network")
  Member "wasserstein weisfeiler lehman graph kernels":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "neural subgraph isomorphism counting")
  Member "graph neural networks for small graph and giant network representation learning an overview":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "segmented graph bert for graph instance modeling")

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)