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: "dictionary learning over distributed models"
  Abstract: In this paper, we consider learning dictionary models over a network of agents, where each agent is only in charge of a portion of the dictionary elements. This formulation is relevant in Big Data scenarios where large dictionary models may be spread over different spatial locations and it is not feasible to aggregate all dictionaries in one location due to communication and privacy considerations. We first show that the dual function of the inference problem is an aggregation of individual cost functions associated with different agents, which can then be minimized efficiently by means of diffusion strategies. The collaborative inference step generates dual variables that are used by the agents to update their dictionaries without the need to share these dictionaries or even the coefficient models for the training data. This is a powerful property that leads to an effective distributed procedure for learning dictionaries over large networks (e.g., hundreds of agents in our experiments). Furthermore, the proposed learning strategy operates in an online manner and is able to respond to streaming data, where each data sample is presented to the network once.

Connected through hyperedge HE-1 (induced by source paper "supervised learning under distributed features"):
  with members:
    - "coordinate descent diffusion learning by networked agents"
    - "variance reduced stochastic learning by networked agents under random reshuffling"
    - "variance reduced stochastic learning under random reshuffling"
    - "distributed learning for stochastic generalized nash equilibrium problems"
    - "sdca without duality regularization and individual convexity"
    - "doubly random parallel stochastic methods for large scale learning"
    - "next in network nonconvex optimization"
    - "multi agent distributed optimization via inexact consensus admm"

Connected through hyperedge HE-2 (induced by source paper "dynamic average diffusion with randomized coordinate updates"):
  with members:
    - "supervised learning under distributed features"
    - "stochastic gradient push for distributed deep learning"
    - "coordinate descent diffusion learning by networked agents"
    - "distributed big data optimization via block iterative convexification and averaging"
    - "next in network nonconvex optimization"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "distributed pareto optimization via diffusion strategies"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"

Connected through hyperedge HE-3 (induced by source paper "distributed low rank adaptive algorithms based on alternating optimization and applications"):
  with members:
    - "widely linear precoding for large scale mimo with iqi algorithms and performance analysis"
    - "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"
    - "single bit and reduced dimension diffusion strategies over distributed networks"
    - "adaptive distributed space time coding based on adjustable code matrices for cooperative mimo relaying systems"
    - "mimo radar using compressive sampling"

Connected through hyperedge HE-4 (induced by source paper "diffusion lms for multitask problems with local linear equality constraints"):
  with members:
    - "proximal multitask learning over networks with sparsity inducing coregularization"
    - "unsupervised diffusion based lms for node specific parameter estimation over wireless sensor networks"
    - "diffusion lms over multitask networks"
    - "distributed diffusion based lms for node specific adaptive parameter estimation"
    - "multitask diffusion adaptation over asynchronous networks"
    - "estimation of space time varying parameters using a diffusion lms algorithm"
    - "on the mean square performance of the constrained lms algorithm"
    - "multitask diffusion adaptation over networks"

Connected through hyperedge HE-5 (induced by source paper "cloud k svd a collaborative dictionary learning algorithm for big distributed data"):
  with members:
    - "multitask diffusion adaptation over networks"
    - "perturbation analysis of orthogonal matching pursuit"
    - "generalized principal component analysis gpca"
    - "hybrid linear modeling via local best fit flats"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"
    - "diland an algorithm for distributed sensor localization with noisy distance measurements"
    - "distributed sensor localization in random environments using minimal number of anchor nodes"

Connected through hyperedge HE-6 (induced by source paper "on the asymptotic bias of the diffusion based distributed pareto optimization"):
  with members:
    - "unsupervised diffusion based lms for node specific parameter estimation over wireless sensor networks"
    - "diffusion lms over multitask networks"
    - "information exchange and learning dynamics over weakly connected adaptive networks"
    - "compressive diffusion strategies over distributed networks for reduced communication load"
    - "distributed diffusion based lms for node specific adaptive parameter estimation"
    - "analysis of a reduced communication diffusion lms algorithm"
    - "distributed pareto optimization via diffusion strategies"
    - "on the learning behavior of adaptive networks part ii performance analysis"

Connected through hyperedge HE-7 (induced by source paper "on the learning behavior of adaptive networks part i transient analysis"):
  with members:
    - "distributed policy evaluation under multiple behavior strategies"
    - "analysis of sum weight like algorithms for averaging in wireless sensor networks"
    - "distributed pareto optimization via diffusion strategies"
    - "on the learning behavior of adaptive networks part ii performance analysis"
    - "diffusion adaptation over networks"
    - "performance limits for distributed estimation over lms adaptive networks"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "gossip algorithms for distributed signal processing"

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)