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: "contextual graph markov model a deep and generative approach to graph processing"
  Abstract: We introduce the Contextual Graph Markov Model, an approach combining ideas from generative models and neural networks for the processing of graph data. It founds on a constructive methodology to build a deep architecture comprising layers of probabilistic models that learn to encode the structured information in an incremental fashion. Context is diffused in an efficient and scalable way across the graph vertexes and edges. The resulting graph encoding is used in combination with discriminative models to address structure classification benchmarks.

Connected through hyperedge HE-1 (induced by source paper "graph signal processing part iii machine learning on graphs from graph topology to applications"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "graphsac detecting anomalies in large scale graphs"
    - "power systems topology and state estimation by graph blind source separation"
    - "semi blind inference of topologies and dynamical processes over dynamic graphs"
    - "graph signal processing part i graphs graph spectra and spectral clustering"
    - "star gcn stacked and reconstructed graph convolutional networks for recommender systems"
    - "graph signal processing part ii processing and analyzing signals on graphs"
    - "an intuitive derivation of the coherence index relation in compressive sensing"

Connected through hyperedge HE-2 (induced by source paper "tensor decompositions in deep learning"):
  with members:
    - "theoretically expressive and edge aware graph learning"
    - "tucker tensor layer in fully connected neural networks"
    - "a fair comparison of graph neural networks for graph classification"
    - "bayesian tensor factorisation for bottom up hidden tree markov models"
    - "a gentle introduction to deep learning for graphs"
    - "how powerful are graph neural networks"
    - "mutan multimodal tucker fusion for visual question answering"
    - "event representations with tensor based compositions"

Connected through hyperedge HE-3 (induced by source paper "theoretically expressive and edge aware graph learning"):
  with members:
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "relational inductive biases deep learning and graph networks"
    - "how powerful are graph neural networks"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "gated graph sequence neural networks"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-4 (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-5 (induced by source paper "graph neural networks with convolutional arma filters"):
  with members:
    - "lanczosnet multi scale deep graph convolutional networks"
    - "on the transferability of spectral graph filters"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "hierarchical graph representation learning with differentiable pooling"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "relational inductive biases deep learning and graph networks"
    - "how powerful are graph neural networks"
    - "autoregressive moving average graph filtering"

Connected through hyperedge HE-6 (induced by source paper "dagcn dual attention graph convolutional networks"):
  with members:
    - "a comprehensive survey on graph neural networks"
    - "on filter size in graph convolutional networks"
    - "adaptive graph convolutional neural networks"
    - "adversarially regularized graph autoencoder for graph embedding"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "watch your step learning node embeddings via graph attention"
    - "cayleynets graph convolutional neural networks with complex rational spectral filters"

Connected through hyperedge HE-7 (induced by source paper "embeddings and representation learning for structured data"):
  with members:
    - "deep weisfeiler lehman assignment kernels via multiple kernel learning"
    - "tree edit distance learning via adaptive symbol embeddings"
    - "constrained graph variational autoencoders for molecule design"
    - "hierarchical graph representation learning with differentiable pooling"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "tree to tree neural networks for program translation"
    - "junction tree variational autoencoder for molecular graph generation"
    - "lstm a search space odyssey"

Connected through hyperedge HE-8 (induced by source paper "a non negative factorization approach to node pooling in graph convolutional neural networks"):
  with members:
    - "graph neural networks with convolutional arma filters"
    - "fast graph representation learning with pytorch geometric"
    - "hierarchical graph representation learning with differentiable pooling"
    - "adversarially regularized graph autoencoder for graph embedding"
    - "how powerful are graph neural networks"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "geometric deep learning going beyond euclidean data"
    - "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)