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: "client server multitask learning from distributed datasets"
  Abstract: A client-server architecture to simultaneously solve multiple learning tasks from distributed datasets is described. In such architecture, each client corresponds to an individual learning task and the associated dataset of examples. The goal of the architecture is to perform information fusion from multiple datasets while preserving privacy of individual data. The role of the server is to collect data in real time from the clients and codify the information in a common database. Such information can be used by all the clients to solve their individual learning task, so that each client can exploit the information content of all the datasets without actually having access to private data of others. The proposed algorithmic framework, based on regularization and kernel methods, uses a suitable class of “mixed effect” kernels. The methodology is illustrated through a simulated recommendation system, as well as an experiment involving pharmacological data coming from a multicentric clinical trial.

Connected through hyperedge HE-1 (induced by source paper "distributed multi task relationship learning"):
  with members:
    - "asynchronous multi task learning"
    - "distributed multi task learning with shared representation"
    - "adding vs averaging in distributed primal dual optimization"
    - "mllib machine learning in apache spark"
    - "communication efficient distributed optimization using an approximate newton type method"
    - "a convex formulation for learning task relationships in multi task learning"
    - "distributed learning communication complexity and privacy"
    - "clustered multi task learning a convex formulation"

Connected through hyperedge HE-2 (induced by source paper "asynchronous multi task learning"):
  with members:
    - "asynchronous distributed optimization using a randomized alternating direction method of multipliers"

Connected through hyperedge HE-3 (induced by source paper "the use of machine learning algorithms in recommender systems a systematic review"):
  with members:
    - "exploration in interactive personalized music recommendation a reinforcement learning approach"
    - "empirical analysis of predictive algorithms for collaborative filtering"

Connected through hyperedge HE-4 (induced by source paper "learning output kernels for multi task problems"):
  with members:
    - "a convex formulation for learning task relationships in multi task learning"
    - "a new approach to collaborative filtering operator estimation with spectral regularization"
    - "learning to learn"

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)