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: "learning the dependence graph of time series with latent factors"
  Abstract: This paper considers the problem of learning, from samples, the dependency structure of a system of linear stochastic differential equations, when some of the variables are latent. In particular, we observe the time evolution of some variables, and never observe other variables; from this, we would like to find the dependency structure between the observed variables - separating out the spurious interactions caused by the (marginalizing out of the) latent variables' time series. We develop a new method, based on convex optimization, to do so in the case when the number of latent variables is smaller than the number of observed ones. For the case when the dependency structure between the observed variables is sparse, we theoretically establish a high-dimensional scaling result for structure recovery. We verify our theoretical result with both synthetic and real data (from the stock market).

Connected through hyperedge HE-1 (induced by source paper "online learning of power transmission dynamics"):
  with members:
    - "optimal topology design for disturbance minimization in power grids"
    - "applying a formula for generator redispatch to damp interarea oscillations using synchrophasors"

Connected through hyperedge HE-2 (induced by source paper "identifying nonlinear 1 step causal influences in presence of latent variables"):
  with members:
    - "topology identification of directed dynamical networks via power spectral analysis"

Connected through hyperedge HE-3 (induced by source paper "learning vector autoregressive models with latent processes"):
  with members:
    - "discovering the hidden structure of complex dynamic systems"

Connected through hyperedge HE-4 (induced by source paper "learning vector autoregressive models with focalised granger causality graphs"):
  with members:
    - "learning task grouping and overlap in multi task learning"
    - "structured sparsity through convex optimization"
    - "clustered multi task learning a convex formulation"

Connected through hyperedge HE-5 (induced by source paper "learning leading indicators for time series predictions"):
  with members:
    - "learning task grouping and overlap in multi task learning"
    - "structured sparsity through convex optimization"
    - "clustered multi task learning a convex formulation"

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)