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: "segregating event streams and noise with a markov renewal process model"
  Abstract: We describe an inference task in which a set of timestamped event observations must be clustered into an unknown number of temporal sequences with independent and varying rates of observations. Various existing approaches to multi-object tracking assume a fixed number of sources and/or a fixed observation rate; we develop an approach to inferring structure in timestamped data produced by a mixture of an unknown and varying number of similar Markov renewal processes, plus independent clutter noise. The inference simultaneously distinguishes signal from noise as well as clustering signal observations into separate source streams. We illustrate the technique via a synthetic experiment as well as an experiment to track a mixture of singing birds.

Connected through hyperedge HE-1 (induced by source paper "spectral visibility graphs application to similarity of harmonic signals"):
  with members:
    - "does k matter k nn hubness analysis for kernel additive modelling vocal separation"
    - "shift invariant kernel additive modelling for audio source separation"
    - "interference reduction in music recordings combining kernel additive modelling and non negative matrix factorization"

Connected through hyperedge HE-2 (induced by source paper "visibility graphs for robust harmonic similarity measures between audio spectra"):
  with members:
    - "shift invariant kernel additive modelling for audio source separation"
    - "interference reduction in music recordings combining kernel additive modelling and non negative matrix factorization"

Connected through hyperedge HE-3 (induced by source paper "intrusions in marked renewal processes"):
  with members:
    - "modeling events with cascades of poisson processes"

Connected through hyperedge HE-4 (induced by source paper "denoising without access to clean data using a partitioned autoencoder"):
  with members:
    - "deep convolutional inverse graphics network"
    - "discovering hidden factors of variation in deep networks"
    - "an analysis of unsupervised pre training in light of recent advances"
    - "exact solutions to the nonlinear dynamics of learning in deep linear neural networks"
    - "improved multiple birdsong tracking with distribution derivative method and markov renewal process clustering"
    - "adadelta an adaptive learning rate method"
    - "theano new features and speed improvements"

Connected through hyperedge HE-5 (induced by source paper "improved multiple birdsong tracking with distribution derivative method and markov renewal process clustering"):
  with members:
    - "variational inference in non negative factorial hidden markov models for efficient audio source separation"

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.AI(Artificial Intelligence)