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: "on valid optimal assignment kernels and applications to graph classification"
  Abstract: The success of kernel methods has initiated the design of novel positive semidefinite functions, in particular for structured data. A leading design paradigm for this is the convolution kernel, which decomposes structured objects into their parts and sums over all pairs of parts. Assignment kernels, in contrast, are obtained from an optimal bijection between parts, which can provide a more valid notion of similarity. In general however, optimal assignments yield indefinite functions, which complicates their use in kernel methods. We characterize a class of base kernels used to compare parts that guarantees positive semidefinite optimal assignment kernels. These base kernels give rise to hierarchies from which the optimal assignment kernels are computed in linear time by histogram intersection. We apply these results by developing the Weisfeiler-Lehman optimal assignment kernel for graphs. It provides high classification accuracy on widely-used benchmark data sets improving over the original Weisfeiler-Lehman kernel.

Connected through hyperedge HE-1 (induced by source paper "memory based graph networks"):
  with members:
    - "mincut pooling in graph neural networks"
    - "clique pooling for graph classification"
    - "graph u nets"
    - "generating diverse high fidelity images with vq vae 2"
    - "relational graph representation learning for open domain question answering"
    - "self attention graph pooling"
    - "unsupervised multi task feature learning on point clouds"
    - "large memory layers with product keys"

Connected through hyperedge HE-2 (induced by source paper "an end to end graph convolutional kernel support vector machine"):
  with members:
    - "scalable global alignment graph kernel using random features from node embedding to graph embedding"
    - "a fair comparison of graph neural networks for graph classification"
    - "on graph classification networks datasets and baselines"
    - "fast graph representation learning with pytorch geometric"
    - "learning discrete structures for graph neural networks"
    - "a comprehensive survey on graph neural networks"
    - "representation learning on graphs with jumping knowledge networks"
    - "graphrnn generating realistic graphs with deep auto regressive models"

Connected through hyperedge HE-3 (induced by source paper "ddgk learning graph representations for deep divergence graph kernels"):
  with members:
    - "is a single embedding enough learning node representations that capture multiple social contexts"
    - "netlsd hearing the shape of a graph"
    - "hierarchical graph representation learning with differentiable pooling"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "a tutorial on network embeddings"
    - "graph edit distance computation via graph neural networks"
    - "verse versatile graph embeddings from similarity measures"
    - "relational inductive biases deep learning and graph networks"

Connected through hyperedge HE-4 (induced by source paper "infograph unsupervised and semi supervised graph level representation learning via mutual information maximization"):
  with members:
    - "utilizing edge features in graph neural networks via variational information maximization"
    - "pre training graph neural networks"
    - "fast graph representation learning with pytorch geometric"
    - "hierarchical graph representation learning with differentiable pooling"
    - "representation learning on graphs with jumping knowledge networks"
    - "how powerful are graph neural networks"
    - "anonymous walk embeddings"
    - "realistic evaluation of deep semi supervised learning algorithms"

Connected through hyperedge HE-5 (induced by source paper "image classification with hierarchical multigraph networks"):
  with members:
    - "graph u nets"
    - "spectral multigraph networks for discovering and fusing relationships in molecules"
    - "iterative visual reasoning beyond convolutions"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "hierarchical graph representation learning with differentiable pooling"
    - "relational inductive biases deep learning and graph networks"
    - "how powerful are graph neural networks"
    - "splinecnn fast geometric deep learning with continuous b spline kernels"

Connected through hyperedge HE-6 (induced by source paper "graph filtration learning"):
  with members:
    - "learning metrics for persistence based summaries and applications for graph classification"
    - "neural persistence a complexity measure for deep neural networks using algebraic topology"
    - "hierarchical graph representation learning with differentiable pooling"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "representation learning on graphs with jumping knowledge networks"
    - "deep sets"
    - "dynamic graph cnn for learning on point clouds"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-7 (induced by source paper "a unifying view of explicit and implicit feature maps of graph kernels"):
  with members:
    - "a survey on graph kernels"
    - "graph convolutional neural networks for web scale recommender systems"
    - "tree based kernel for graphs with continuous attributes"
    - "representation learning on graphs methods and applications"
    - "neural message passing for quantum chemistry"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "subgraph2vec learning distributed representations of rooted sub graphs from large graphs"

Connected through hyperedge HE-8 (induced by source paper "scalable global alignment graph kernel using random features from node embedding to graph embedding"):
  with members:
    - "ddgk learning graph representations for deep divergence graph kernels"
    - "revisiting random binning features fast convergence and strong parallelizability"
    - "scalable spectral clustering using random binning features"
    - "random warping series a random features method for time series embedding"
    - "sparse diffusion convolutional neural networks"
    - "dgcnn disordered graph convolutional neural network based on the gaussian mixture model"
    - "learning convolutional neural networks for graphs"
    - "primme_svds a high performance preconditioned svd solver for accurate large scale computations"

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)