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: "graph matching networks for learning the similarity of graph structured objects"
  Abstract: This paper addresses the challenging problem of retrieval and matching of graph structured objects, and makes two key contributions. First, we demonstrate how Graph Neural Networks (GNN), which have emerged as an effective model for various supervised prediction problems defined on structured data, can be trained to produce embedding of graphs in vector spaces that enables efficient similarity reasoning. Second, we propose a novel Graph Matching Network model that, given a pair of graphs as input, computes a similarity score between them by jointly reasoning on the pair through a new cross-graph attention-based matching mechanism. We demonstrate the effectiveness of our models on different domains including the challenging problem of control-flow-graph based function similarity search that plays an important role in the detection of vulnerabilities in software systems. The experimental analysis demonstrates that our models are not only able to exploit structure in the context of similarity learning but they can also outperform domain-specific baseline systems that have been carefully hand-engineered for these problems.

Connected through hyperedge HE-1 (induced by source paper "deep graph matching consensus"):
  with members:
    - "simgnn a neural network approach to fast graph similarity computation"
    - "learning combinatorial embedding networks for deep graph matching"
    - "cross lingual knowledge graph alignment via graph matching neural network"
    - "computing optimal assignments in linear time for approximate graph matching"
    - "scalable gromov wasserstein learning for graph partitioning and matching"
    - "deep adversarial network alignment"
    - "graph neural networks for user identity linkage"
    - "kergm kernelized graph matching"

Connected through hyperedge HE-2 (induced by source paper "fast detection of maximum common subgraph via deep q learning"):
  with members:
    - "simgnn a neural network approach to fast graph similarity computation"
    - "learning combinatorial embedding networks for deep graph matching"
    - "learning to route in similarity graphs"
    - "a unifying framework for spectrum preserving graph sparsification and coarsening"
    - "scalable gromov wasserstein learning for graph partitioning and matching"
    - "gromov wasserstein learning for graph matching and node embedding"
    - "a learning based branch and bound for maximum common subgraph problems"
    - "fast graph representation learning with pytorch geometric"

Connected through hyperedge HE-3 (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"
    - "isonn isomorphic neural network for graph representation learning and classification"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "detecting code clones with graph neural networkand flow augmented abstract syntax tree"):
  with members:
    - "fast graph representation learning with pytorch geometric"
    - "graph neural networks a review of methods and applications"
    - "convolutional neural networks over control flow graphs for software defect prediction"
    - "oreo detection of clones in the twilight zone"
    - "generative code modeling with graphs"
    - "neural message passing for quantum chemistry"
    - "learning to represent programs with graphs"
    - "gated graph sequence neural networks"

Connected through hyperedge HE-5 (induced by source paper "an overview of distance and similarity functions for structured data"):
  with members:
    - "relational inductive biases deep learning and graph networks"
    - "how powerful are graph neural networks"
    - "an empirical evaluation of similarity measures for time series classification"
    - "a survey on metric learning for feature vectors and structured data"
    - "time warp edit distance with stiffness adjustment for time series matching"
    - "support vector machine classification with indefinite kernels"
    - "semantic similarity in a taxonomy an information based measure and its application to problems of ambiguity in natural language"

Connected through hyperedge HE-6 (induced by source paper "towards learning representations of binary executable files for security tasks"):
  with members:
    - "neural code comprehension a learnable representation of code semantics"
    - "vuldeepecker a deep learning based system for vulnerability detection"
    - "neural machine translation inspired binary code similarity comparison beyond function pairs"
    - "neural network based graph embedding for cross platform binary code similarity detection"
    - "robust and effective malware detection through quantitative data flow graph metrics"
    - "on the naturalness of buggy code"

Connected through hyperedge HE-7 (induced by source paper "deep graph similarity learning a survey"):
  with members:
    - "simgnn a neural network approach to fast graph similarity computation"
    - "deep graph matching consensus"
    - "dynamic graph convolutional networks"
    - "glmnet graph learning matching networks for feature matching"
    - "from community to role based graph embeddings"
    - "learning combinatorial embedding networks for deep graph matching"
    - "community preserving graph convolutions for structural and functional joint embedding of brain networks"
    - "generalizing from a few examples a survey on few shot learning"

Connected through hyperedge HE-8 (induced by source paper "superglue learning feature matching with graph neural networks"):
  with members:
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "dual graph convolutional network for semantic segmentation"
    - "contextdesc local descriptor augmentation with cross modality context"
    - "neural guided ransac learning where to sample model hypotheses"
    - "deep closest point learning representations for point cloud registration"
    - "r2d2 repeatable and reliable detector and descriptor"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "set transformer a framework for attention based permutation invariant neural 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)