human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "isonn isomorphic neural network for graph representation learning and classification"
  Abstract: Deep learning models have achieved huge success in numerous fields, such as computer vision and natural language processing. However, unlike such fields, it is hard to apply traditional deep learning models on the graph data due to the 'node-orderless' property. Normally, adjacency matrices will cast an artificial and random node-order on the graphs, which renders the performance of deep models on graph classification tasks extremely erratic, and the representations learned by such models lack clear interpretability. To eliminate the unnecessary node-order constraint, we propose a novel model named Isomorphic Neural Network (IsoNN), which learns the graph representation by extracting its isomorphic features via the graph matching between input graph and templates. IsoNN has two main components: graph isomorphic feature extraction component and classification component. The graph isomorphic feature extraction component utilizes a set of subgraph templates as the kernel variables to learn the possible subgraph patterns existing in the input graph and then computes the isomorphic features. A set of permutation matrices is used in the component to break the node-order brought by the matrix representation. Three fully-connected layers are used as the classification component in IsoNN. Extensive experiments are conducted on benchmark datasets, the experimental results can demonstrate the effectiveness of ISONN, especially compared with both classic and state-of-the-art graph classification methods.

Connected through hyperedge HE-1 (induced by source paper "graph neural distance metric learning with graph bert"):
  with members:
    - "simgnn a neural network approach to fast graph similarity computation"
    - "graph bert only attention is needed for learning graph representations"
    - "adagcn adaboosting graph convolutional networks into deep models"
    - "structure fusion based on graph convolutional networks for semi supervised classification"
    - "gresnet graph residual network for reviving deep gnns from suspended animation"
    - "wasserstein weisfeiler lehman graph kernels"
    - "graph neural networks for small graph and giant network representation learning an overview"
    - "graph matching networks for learning the similarity of graph structured objects"

Connected through hyperedge HE-2 (induced by source paper "segmented graph bert for graph instance modeling"):
  with members:
    - "graph bert only attention is needed for learning graph representations"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "dagcn dual attention graph convolutional networks"
    - "asap adaptive structure aware pooling for learning hierarchical graph representations"
    - "capsule neural networks for graph classification using explicit tensorial graph representations"
    - "gresnet graph residual network for reviving deep gnns from suspended animation"
    - "graph neural networks for small graph and giant network representation learning an overview"
    - "roberta a robustly optimized bert pretraining approach"

Connected through hyperedge HE-3 (induced by source paper "graph bert only attention is needed for learning graph representations"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "gresnet graph residual network for reviving deep gnns from suspended animation"
    - "segen sample ensemble genetic evolutionary network model"
    - "rethinking imagenet pre training"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deep loopy neural network model for graph structured data representation learning"
    - "junction tree variational autoencoder for molecular graph generation"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "graph neural networks for small graph and giant network representation learning an overview"):
  with members:
    - "graph neural lasso for dynamic network regression"
    - "segen sample ensemble genetic evolutionary network model"
    - "semi supervised classification with graph convolutional networks"

Question: Please tell me which class the center node 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)