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: "the augmented complex kernel lms"
  Abstract: Recently, a unified framework for adaptive kernel based signal processing of complex data was presented by the authors, which, besides offering techniques to map the input data to complex reproducing kernel Hilbert spaces, developed a suitable Wirtinger-like calculus for general Hilbert spaces. In this short paper, the extended Wirtinger's calculus is adopted to derive complex kernel-based widely linear estimation filters suitable for applications involving noncircular data. Furthermore, we illuminate several important characteristics of the widely linear filters. We show that, although in many cases the gains from adopting widely linear estimation filters, as alternatives to ordinary linear ones, are rudimentary, for the case of kernel based widely linear filters significant performance improvements can be obtained.

Connected through hyperedge HE-1 (induced by source paper "complex gaussian processes for regression"):
  with members:
    - "gaussian processes for nonlinear signal processing an overview of recent advances"
    - "extension of wirtinger s calculus to reproducing kernel hilbert spaces and the complex kernel lms"

Connected through hyperedge HE-2 (induced by source paper "a diffusion kernel lms algorithm for nonlinear adaptive networks"):
  with members:
    - "distributed diffusion based lms for node specific adaptive parameter estimation"
    - "multitask diffusion adaptation over networks"
    - "diffusion adaptation over networks"
    - "extension of wirtinger s calculus to reproducing kernel hilbert spaces and the complex kernel lms"

Connected through hyperedge HE-3 (induced by source paper "proper complex gaussian processes for regression"):
  with members:
    - "gaussian processes for nonlinear signal processing an overview of recent advances"
    - "complex valued random vectors and channels entropy divergence and capacity"
    - "extension of wirtinger s calculus to reproducing kernel hilbert spaces and the complex kernel lms"

Connected through hyperedge HE-4 (induced by source paper "complex support vector machines for regression and quaternary classification"):
  with members:
    - "extension of wirtinger s calculus to reproducing kernel hilbert spaces and the complex kernel lms"
    - "consistency of the group lasso and multiple kernel learning"

Connected through hyperedge HE-5 (induced by source paper "complex valued gaussian processes for regression a widely non linear approach"):
  with members:
    - "gaussian processes for nonlinear signal processing an overview of recent advances"

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)