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: "weighted spectral embedding of graphs"
  Abstract: We present a novel spectral embedding of graphs that incorporates weights assigned to the nodes, quantifying their relative importance. This spectral embedding is based on the first eigenvectors of some properly normalized version of the Laplacian. We prove that these eigenvectors correspond to the configurations of lowest energy of an equivalent physical system, either mechanical or electrical, in which the weight of each node can be interpreted as its mass or its capacitance, respectively. Experiments on a real dataset illustrate the impact of weighting on the embedding.

Connected through hyperedge HE-1 (induced by source paper "using laplacian spectrum as graph feature representation"):
  with members:
    - "variational recurrent neural networks for graph classification"
    - "a simple baseline algorithm for graph classification"
    - "graph similarity and approximate isomorphism"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "how powerful are graph neural networks"
    - "dynamic routing between capsules"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"

Connected through hyperedge HE-2 (induced by source paper "spectral embedding of regularized block models"):
  with members:
    - "pseudo likelihood methods for community detection in large sparse networks"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-3 (induced by source paper "a simple baseline algorithm for graph classification"):
  with members:
    - "mixed batches and symmetric discriminators for gan training"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "learning graph level representations with gated recurrent neural networks"
    - "kernel graph convolutional neural networks"
    - "high order stochastic graphlet embedding for graph based pattern recognition"
    - "feature based classification of 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)