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: "conditional chow liu tree structures for modeling discrete valued vector time series"
  Abstract: We consider the problem of modeling discrete-valued vector time series data using extensions of Chow-Liu tree models to capture both dependencies across time and dependencies across variables. Conditional Chow-Liu tree models are introduced, as an extension to standard Chow-Liu trees, for modeling conditional rather than joint densities. We describe learning algorithms for such models and show how they can be used to learn parsimonious representations for the output distributions in hidden Markov models. These models are applied to the important problem of simulating and forecasting daily precipitation occurrence for networks of rain stations. To demonstrate the effectiveness of the models, we compare their performance versus a number of alternatives using historical precipitation data from Southwestern Australia and the Western United States. We illustrate how the structure and parameters of the models can be used to provide an improved meteorological interpretation of such data.

Connected through hyperedge HE-1 (induced by source paper "to frontalize or not to frontalize a study of face pre processing techniques and their impact on recognition"):
  with members:
    - "the do s and don ts for cnn based face verification"
    - "do we really need to collect millions of faces for effective face recognition"
    - "face detection with the faster r cnn"
    - "robust face alignment using a mixture of invariant experts"
    - "face recognition using deep multi pose representations"
    - "finding tiny faces"
    - "robust face recognition via multimodal deep face representation"
    - "deepid3 face recognition with very deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "learning gaussian graphical models with observed or latent fvss"):
  with members:
    - "a constraint propagation approach to probabilistic reasoning"

Connected through hyperedge HE-3 (induced by source paper "learning selectively conditioned forest structures with applications to dbns and classification"):
  with members:
    - "tractable bayesian learning of tree belief networks"
    - "learning the structure of dynamic probabilistic networks"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "induction of selective bayesian classifiers"
    - "theory refinement on bayesian networks"
    - "being bayesian about network structure"

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)