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: "anonymous walk embeddings"
  Abstract: The task of representing entire graphs has seen a surge of prominent results, mainly due to learning convolutional neural networks (CNNs) on graph-structured data. While CNNs demonstrate state-of-the-art performance in graph classification task, such methods are supervised and therefore steer away from the original problem of network representation in task-agnostic manner. Here, we coherently propose an approach for embedding entire graphs and show that our feature representations with SVM classifier increase classification accuracy of CNN algorithms and traditional graph kernels. For this we describe a recently discovered graph object, anonymous walk, on which we design task-independent algorithms for learning graph representations in explicit and distributed way. Overall, our work represents a new scalable unsupervised learning of state-of-the-art representations of entire graphs.

Connected through hyperedge HE-1 (induced by source paper "few shot learning on graphs via super classes based on graph spectral measures"):
  with members:
    - "a survey on graph kernels"
    - "self attention graph pooling"
    - "a closer look at few shot classification"
    - "a comprehensive survey on graph neural networks"
    - "on first order meta learning algorithms"
    - "meta learning with latent embedding optimization"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "relational inductive biases deep learning and graph networks"

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 "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"
    - "isonn isomorphic neural network for graph representation learning and classification"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "learning distributed representations of graphs with geo2dr"):
  with members:
    - "on graph classification networks datasets and baselines"
    - "deep graph library towards efficient and scalable deep learning on graphs"
    - "fast graph representation learning with pytorch geometric"
    - "relational inductive biases deep learning and graph networks"
    - "graph embedding techniques applications and performance a survey"
    - "how powerful are graph neural networks"
    - "neural message passing for quantum chemistry"
    - "graph2vec learning distributed representations of graphs"

Connected through hyperedge HE-5 (induced by source paper "a lagrangian approach to information propagation in graph neural networks"):
  with members:
    - "how powerful are graph neural networks"
    - "geometric deep learning going beyond euclidean data"
    - "gated graph sequence neural networks"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"
    - "training neural networks without gradients a scalable admm approach"
    - "deep convolutional networks on graph structured data"
    - "diffusion convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "a gentle introduction to deep learning for graphs"):
  with members:
    - "deep tree transductions a short survey"
    - "gmnn graph markov neural networks"
    - "a model to search for synthesizable molecules"
    - "graph u nets"
    - "a fair comparison of graph neural networks for graph classification"
    - "self attention graph pooling"
    - "deep graph library towards efficient and scalable deep learning on graphs"
    - "fast and deep graph neural networks"

Connected through hyperedge HE-7 (induced by source paper "learning universal graph neural network embeddings with aid of transfer learning"):
  with members:
    - "stability and generalization of graph convolutional neural networks"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "adaptive graph convolutional neural networks"
    - "the natural language decathlon multitask learning as question answering"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "hierarchical graph representation learning with differentiable pooling"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "graphrnn generating realistic graphs with deep auto regressive models"

Connected through hyperedge HE-8 (induced by source paper "provably powerful graph networks"):
  with members:
    - "universal invariant and equivariant graph neural networks"
    - "towards a practical k dimensional weisfeiler leman algorithm"
    - "on the universality of invariant networks"
    - "a survey on graph kernels"
    - "fast graph representation learning with pytorch geometric"
    - "relational pooling for graph representations"
    - "dual primal graph convolutional networks"
    - "hierarchical graph representation learning with differentiable pooling"

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)