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: "spectral embedding norm looking deep into the spectrum of the graph laplacian"
  Abstract: The extraction of clusters from a dataset which includes multiple clusters and another significant portion of "background" samples is a task of practical importance. The traditional spectral clustering algorithm, relying on the leading $K$ eigenvectors to detect the $K$ clusters, fails in such cases. This paper proposes the spectral embedding norm which sums the squared values of the first $I$ (normalized) eigenvectors, where $I$ can be larger than $K$. We prove that the quantity can be used to separate clusters from the background under generic conditions motivated by applications such as anomaly detection. The performance of the algorithm is not sensitive to the choice of $I$, and we present experiments on synthetic and real-world datasets.

Connected through hyperedge HE-1 (induced by source paper "option discovery in the absence of rewards with manifold analysis"):
  with members:
    - "the laplacian in rl learning representations with efficient approximations"
    - "diversity is all you need learning skills without a reward function"
    - "q learning with nearest neighbors"
    - "eigenoption discovery through the deep successor representation"
    - "curiosity driven exploration by self supervised prediction"
    - "stochastic neural networks for hierarchical reinforcement learning"
    - "a laplacian framework for option discovery in reinforcement learning"
    - "the option critic architecture"

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)