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: "multi scale attributed node embedding"
  Abstract: We present network embedding algorithms that capture information about a node from the local distribution over node attributes around it, as observed over random walks following an approach similar to Skip-gram. Observations from neighborhoods of different sizes are either pooled (AE) or encoded distinctly in a multi-scale approach (MUSAE). Capturing attribute-neighborhood relationships over multiple scales is useful for a diverse range of applications, including latent feature identification across disconnected networks with similar attributes. We prove theoretically that matrices of node-feature pointwise mutual information are implicitly factorized by the embeddings. Experiments show that our algorithms are robust, computationally efficient and outperform comparable models on social, web and citation network datasets.

Connected through hyperedge HE-1 (induced by source paper "geom gcn geometric graph convolutional networks"):
  with members:
    - "simplifying graph convolutional networks"
    - "mcne an end to end framework for learning multiple conditional network representations of social network"
    - "community detection on networks with ricci flow"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "representation learning on graphs with jumping knowledge networks"
    - "large scale learnable graph convolutional networks"
    - "relational inductive biases deep learning and graph networks"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"

Connected through hyperedge HE-2 (induced by source paper "an api oriented open source python framework for unsupervised learning on graphs"):
  with members:
    - "fast sequence based embedding with diffusion graphs"
    - "scipy 1 0 fundamental algorithms for scientific computing in python"
    - "edmot an edge enhancement approach for motif aware community detection"
    - "pytorch an imperative style high performance deep learning library"
    - "netlsd hearing the shape of a graph"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "a simple baseline algorithm for graph classification"
    - "learning structural node embeddings via diffusion wavelets"

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)