human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a diffusion kernel lms algorithm for nonlinear adaptive networks"):
  Abstract: This work presents a distributed algorithm for nonlinear adaptive learning. In particular, a set of nodes obtain measurements, sequentially one per time step, which are related via a nonlinear function; their goal is to collectively minimize a cost function by employing a diffusion based Kernel Least Mean Squares (KLMS). The algorithm follows the Adapt Then Combine mode of cooperation. Moreover, the theoretical properties of the algorithm are studied and it is proved that under certain assumptions the algorithm suffers a no regret bound. Finally, comparative experiments verify that the proposed scheme outperforms other variants of the LMS.

Includes members:
  Member "distributed diffusion based lms for node specific adaptive parameter estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adaptation and learning over networks under subspace constraints part i stability analysis")
  Member "multitask diffusion adaptation over networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adaptation and learning over networks under subspace constraints part i stability analysis")
  Member "diffusion adaptation over networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sparsity constrained distributed unmixing of hyperspectral data")
  Member "the augmented complex kernel lms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "complex gaussian processes for regression")
  Member "extension of wirtinger s calculus to reproducing kernel hilbert spaces and the complex kernel lms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "widely linear kernels for complex valued kernel activation functions")

Question: Please tell me which class the source paper 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.IT(Information Theory)