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: "extension of wirtinger s calculus to reproducing kernel hilbert spaces and the complex kernel lms"
  Abstract: Over the last decade, kernel methods for nonlinear processing have successfully been used in the machine learning community. The primary mathematical tool employed in these methods is the notion of the reproducing kernel Hilbert space (RKHS). However, so far, the emphasis has been on batch techniques. It is only recently, that online techniques have been considered in the context of adaptive signal processing tasks. Moreover, these efforts have only been focussed on real valued data sequences. To the best of our knowledge, no adaptive kernel-based strategy has been developed, so far, for complex valued signals. Furthermore, although the real reproducing kernels are used in an increasing number of machine learning problems, complex kernels have not, yet, been used, in spite of their potential interest in applications that deal with complex signals, with Communications being a typical example. In this paper, we present a general framework to attack the problem of adaptive filtering of complex signals, using either real reproducing kernels, taking advantage of a technique called complexification of real RKHSs, or complex reproducing kernels, highlighting the use of the complex Gaussian kernel. In order to derive gradients of operators that need to be defined on the associated complex RKHSs, we employ the powerful tool of Wirtinger's Calculus, which has recently attracted attention in the signal processing community. Wirtinger's calculus simplifies computations and offers an elegant tool for treating complex signals. To this end, in this paper, the notion of Wirtinger's calculus is extended, for the first time, to include complex RKHSs and use it to derive several realizations of the complex kernel least-mean-square (CKLMS) algorithm. Experiments verify that the CKLMS offers significant performance improvements over several linear and nonlinear algorithms, when dealing with nonlinearities.

Connected through hyperedge HE-1 (induced by source paper "widely linear kernels for complex valued kernel activation functions"):
  with members:
    - "complex evolution recurrent neural networks cernns"
    - "complex gaussian processes for regression"
    - "deep complex networks"
    - "emnist an extension of mnist to handwritten letters"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "on complex valued convolutional neural networks"
    - "complex support vector machines for regression and quaternary classification"
    - "unitary evolution recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "complex valued neural networks with non parametric activation functions"):
  with members:
    - "self normalizing neural networks"
    - "better than real complex valued neural nets for mri fingerprinting"
    - "deep complex networks"
    - "swish a self gated activation function"
    - "associative long short term memory"
    - "on complex valued convolutional neural networks"
    - "unitary evolution recurrent neural networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-3 (induced by source paper "online distributed learning over networks in rkh spaces using random fourier features"):
  with members:
    - "a diffusion kernel lms algorithm for nonlinear adaptive networks"
    - "efficient klms and krls algorithms a random fourier feature perspective"
    - "on the error of random fourier features"
    - "distributed diffusion based lms for node specific adaptive parameter estimation"
    - "multitask diffusion adaptation over networks"
    - "diffusion adaptation over networks"
    - "gossip algorithms for distributed signal processing"

Connected through hyperedge HE-4 (induced by source paper "complex gaussian processes for regression"):
  with members:
    - "gaussian processes for nonlinear signal processing an overview of recent advances"
    - "the augmented complex kernel lms"

Connected through hyperedge HE-5 (induced by source paper "study of set membership kernel adaptive algorithms and applications"):
  with members:
    - "robust adaptive beamforming based on low rank and cross correlation techniques"
    - "adaptive reduced rank receive processing based on minimum symbol error rate criterion for large scale multiple antenna systems"
    - "robust adaptive beamforming using a low complexity shrinkage based mismatch estimation algorithm"

Connected through hyperedge HE-6 (induced by source paper "on the computation of complex valued gradients with application to statistically optimum beamforming"):
  with members:
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"

Connected through hyperedge HE-7 (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"
    - "the augmented complex kernel lms"

Connected through hyperedge HE-8 (induced by source paper "efficient klms and krls algorithms a random fourier feature perspective"):
  with members:
    - "a diffusion kernel lms algorithm for nonlinear adaptive networks"

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)