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: "gossip learning with linear models on fully distributed data"
  Abstract: Machine learning over fully distributed data poses an important problem in peer-to-peer (P2P) applications. In this model we have one data record at each network node, but without the possibility to move raw data due to privacy considerations. For example, user profiles, ratings, history, or sensor readings can represent this case. This problem is difficult, because there is no possibility to learn local models, the system model offers almost no guarantees for reliability, yet the communication cost needs to be kept low. Here we propose gossip learning, a generic approach that is based on multiple models taking random walks over the network in parallel, while applying an online learning algorithm to improve themselves, and getting combined via ensemble learning methods. We present an instantiation of this approach for the case of classification with linear models. Our main contribution is an ensemble learning method which---through the continuous combination of the models in the network---implements a virtual weighted voting mechanism over an exponential number of models at practically no extra cost as compared to independent random walks. We prove the convergence of the method theoretically, and perform extensive experiments on benchmark datasets. Our experimental analysis demonstrates the performance and robustness of the proposed approach.

Connected through hyperedge HE-1 (induced by source paper "a survey on distributed machine learning"):
  with members:
    - "mxnet mpi embedding mpi parallelism in parameter server task model for scaling deep learning"
    - "how to backdoor federated learning"
    - "no peek a survey of private distributed deep learning"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "poseidon an efficient communication architecture for distributed deep learning on gpu clusters"
    - "scaling deep learning on gpu and knights landing clusters"
    - "revisiting distributed synchronous sgd"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-2 (induced by source paper "securegbm secure multi party gradient boosting"):
  with members:
    - "xgboost a scalable tree boosting system"
    - "predicting the future relevance of research institutions the winning solution of the kdd cup 2016"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"

Connected through hyperedge HE-3 (induced by source paper "a riemannian gossip approach to subspace learning on grassmann manifold"):
  with members:
    - "riemannian stochastic variance reduced gradient algorithm with retraction and vector transport"
    - "a survey on multi task learning"
    - "dimensionality reduction on spd manifolds the emergence of geometry aware methods"
    - "a riemannian gossip approach to decentralized matrix completion"
    - "manopt a matlab toolbox for optimization on manifolds"
    - "a convex formulation for learning task relationships in multi task learning"
    - "fixed rank matrix factorizations and riemannian low rank optimization"
    - "a convex feature learning formulation for latent task structure discovery"

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)