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: "diffusion least mean p power algorithms for distributed estimation in alpha stable noise environments"
  Abstract: A diffusion least-mean P-power (LMP) algorithm is proposed for distributed estimation in alpha-stable noise environments, which is one of the widely used models that appears in various environments. Compared with the diffusion least-mean squares algorithm, better performance is obtained for the diffusion LMP methods when the noise is with alpha-stable distribution.

Connected through hyperedge HE-1 (induced by source paper "study of robust distributed diffusion rls algorithms with side information for adaptive networks"):
  with members:
    - "maximum total correntropy diffusion adaptation over networks with noisy links"
    - "decentralized rls with data adaptive censoring for regressions over large scale networks"
    - "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"
    - "direction finding algorithms based on joint iterative subspace optimization"
    - "distributed pareto optimization via diffusion strategies"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"

Connected through hyperedge HE-2 (induced by source paper "clustered multitask nonnegative matrix factorization for spectral unmixing of hyperspectral data"):
  with members:
    - "sparsity constrained distributed unmixing of hyperspectral data"
    - "hyperspectral unmixing based on clustered multitask networks"
    - "spectral unmixing of hyperspectral imagery using multilayer nmf"
    - "structured sparse method for hyperspectral unmixing"
    - "hyperspectral data unmixing using gnmf method and sparseness constraint"
    - "multitask diffusion adaptation over networks"

Connected through hyperedge HE-3 (induced by source paper "sparsity constrained distributed unmixing of hyperspectral data"):
  with members:
    - "distributed unmixing of hyperspectral data with sparsity constraint"
    - "sparsity constrained graph regularized nmf for spectral unmixing of hyperspectral data"
    - "spectral unmixing of hyperspectral imagery using multilayer nmf"
    - "multilayer structured nmf for spectral unmixing of hyperspectral images"
    - "multitask diffusion adaptation over networks"
    - "diffusion adaptation over networks"

Connected through hyperedge HE-4 (induced by source paper "robust distributed diffusion recursive least squares algorithms with side information for adaptive networks"):
  with members:
    - "maximum total correntropy diffusion adaptation over networks with noisy links"
    - "decentralized rls with data adaptive censoring for regressions over large scale networks"
    - "distributed pareto optimization via diffusion strategies"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"

Connected through hyperedge HE-5 (induced by source paper "maximum total correntropy diffusion adaptation over networks with noisy links"):
  with members:
    - "diffusion lms strategies in sensor networks with noisy input data"
    - "diffusion adaptation over networks"

Connected through hyperedge HE-6 (induced by source paper "a class of diffusion algorithms with logarithmic cost over adaptive sparse volterra network"):
  with members:
    - "diffusion lms strategies in sensor networks with noisy input data"
    - "distributed decision making over adaptive networks"
    - "a novel family of adaptive filtering algorithms based on the logarithmic cost"
    - "estimation of space time varying parameters using a diffusion lms algorithm"
    - "performance limits for distributed estimation over lms adaptive networks"

Connected through hyperedge HE-7 (induced by source paper "maximum correntropy criterion based sparse adaptive filtering algorithms for robust channel estimation under non gaussian environments"):
  with members:
    - "variable earns profit improved adaptive channel estimation using sparse vss nlms algorithms"
    - "a stochastic gradient approach on compressive sensing signal reconstruction based on adaptive filtering framework"

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.IT(Information Theory)