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: "gmnn graph markov neural networks"
  Abstract: This paper studies semi-supervised object classification in relational data, which is a fundamental problem in relational data modeling. The problem has been extensively studied in the literature of both statistical relational learning (e.g. relational Markov networks) and graph neural networks (e.g. graph convolutional networks). Statistical relational learning methods can effectively model the dependency of object labels through conditional random fields for collective classification, whereas graph neural networks learn effective object representations for classification through end-to-end training. In this paper, we propose the Graph Markov Neural Network (GMNN) that combines the advantages of both worlds. A GMNN models the joint distribution of object labels with a conditional random field, which can be effectively trained with the variational EM algorithm. In the E-step, one graph neural network learns effective object representations for approximating the posterior distributions of object labels. In the M-step, another graph neural network is used to model the local label dependency. Experiments on object classification, link classification, and unsupervised node representation learning show that GMNN achieves state-of-the-art results.

Connected through hyperedge HE-1 (induced by source paper "graph representation learning via graphical mutual information maximization"):
  with members:
    - "a two step graph convolutional decoder for molecule generation"
    - "graph wavelet neural network"
    - "relation aware entity alignment for heterogeneous knowledge graphs"
    - "demo net degree specific graph neural networks for node and graph classification"
    - "link prediction based on graph neural networks"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "regal representation learning based graph alignment"
    - "deep sets"

Connected through hyperedge HE-2 (induced by source paper "self supervised graph representation learning via global context prediction"):
  with members:
    - "self supervised dialogue learning"
    - "multi stage self supervised learning for graph convolutional networks"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "attributed graph clustering via adaptive graph convolution"
    - "graph wavelet neural network"
    - "grasp2vec learning object representations from self supervised grasping"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "adaptive graph convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "efficient probabilistic logic reasoning with graph neural networks"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "provably powerful graph networks"
    - "probabilistic logic neural networks for reasoning"
    - "relational inductive biases deep learning and graph networks"
    - "how powerful are graph neural networks"
    - "one shot relational learning for knowledge graphs"
    - "knowledge base completion baselines strike back"
    - "gated graph sequence neural networks"

Connected through hyperedge HE-4 (induced by source paper "estimating aggregate properties in relational networks with unobserved data"):
  with members:
    - "graph summarization methods and applications a survey"
    - "tuffy scaling up statistical inference in markov logic networks using an rdbms"

Connected through hyperedge HE-5 (induced by source paper "a gentle introduction to deep learning for graphs"):
  with members:
    - "deep tree transductions a short survey"
    - "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"
    - "fast graph representation learning with pytorch geometric"

Connected through hyperedge HE-6 (induced by source paper "attributed graph learning with 2 d graph convolution"):
  with members:
    - "lanczosnet multi scale deep graph convolutional networks"
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "attributed graph clustering via adaptive graph convolution"
    - "learning discrete structures for graph neural networks"
    - "label efficient semi supervised learning via graph filtering"
    - "a flexible generative framework for graph based semi supervised learning"
    - "graph neural networks a review of methods and applications"
    - "deep learning on graphs a survey"

Connected through hyperedge HE-7 (induced by source paper "graphmix regularized training of graph neural networks for semi supervised learning"):
  with members:
    - "graph u nets"
    - "mixmatch a holistic approach to semi supervised learning"
    - "graph adversarial training dynamically regularizing based on graph structure"
    - "batch virtual adversarial training for graph convolutional networks"
    - "graph neural networks a review of methods and applications"
    - "understanding and improving interpolation in autoencoders via an adversarial regularizer"
    - "dropblock a regularization method for convolutional networks"
    - "pitfalls of graph neural network evaluation"

Connected through hyperedge HE-8 (induced by source paper "continuous graph neural networks"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "break the ceiling stronger multi scale deep graph convolutional networks"
    - "diffusion improves graph learning"
    - "understanding the representation power of graph neural networks in learning graph topology"
    - "discrete and continuous deep residual learning over graphs"
    - "latent odes for irregularly sampled time series"
    - "graph neural networks exponentially lose expressive power for node classification"
    - "simplifying 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)