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 "study of robust distributed diffusion rls algorithms with side information for adaptive networks"):
  Abstract: This work develops robust diffusion recursive least squares algorithms to mitigate the performance degradation often experienced in networks of agents in the presence of impulsive noise. The first algorithm minimizes an exponentially weighted least-squares cost function subject to a time-dependent constraint on the squared norm of the intermediate update at each node. A recursive strategy for computing the constraint is proposed using side information from the neighboring nodes to further improve the robustness. We also analyze the mean-square convergence behavior of the proposed algorithm. The second proposed algorithm is a modification of the first one based on the dichotomous coordinate descent iterations. It has a performance similar to that of the former, however its complexity is significantly lower especially when input regressors of agents have a shift structure and it is well suited to practical implementation. Simulations show the superiority of the proposed algorithms over previously reported techniques in various impulsive noise scenarios.

Includes members:
  Member "maximum total correntropy diffusion adaptation over networks with noisy links":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "robust distributed diffusion recursive least squares algorithms with side information for adaptive networks")
  Member "decentralized rls with data adaptive censoring for regressions over large scale networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust distributed diffusion recursive least squares algorithms with side information for adaptive networks")
  Member "robust adaptive beamforming based on low rank and cross correlation techniques":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "study of coarse quantization aware block diagonalization algorithms for mimo systems with low resolution")
  Member "adaptive reduced rank receive processing based on minimum symbol error rate criterion for large scale multiple antenna systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "study of coarse quantization aware block diagonalization algorithms for mimo systems with low resolution")
  Member "robust adaptive beamforming using a low complexity shrinkage based mismatch estimation algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "study of coarse quantization aware block diagonalization algorithms for mimo systems with low resolution")
  Member "direction finding algorithms based on joint iterative subspace optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "study of coarse quantization aware block diagonalization algorithms for mimo systems with low resolution")
  Member "diffusion least mean p power algorithms for distributed estimation in alpha stable noise environments":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "clustered multitask nonnegative matrix factorization for spectral unmixing of hyperspectral data")
  Member "distributed pareto optimization via diffusion strategies":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "supervised learning under distributed features")

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.LG(Machine Learning)