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: "federated over the air subspace learning from incomplete data"
  Abstract: Federated learning refers to a distributed learning scenario in which users/nodes keep their data private but only share intermediate locally computed iterates with the master node. The master, in turn, shares a global aggregate of these iterates with all the nodes at each iteration. In this work, we consider a wireless federated learning scenario where the nodes communicate to and from the master node via a wireless channel. Current and upcoming technologies such as 5G (and beyond) will operate mostly in a non-orthogonal multiple access (NOMA) mode where transmissions from the users occupy the same bandwidth and interfere at the access point. These technologies naturally lend themselves to an "over-the-air" superposition whereby information received from the user nodes can be directly summed at the master node. #R##N#However, over-the-air aggregation also means that the channel noise can corrupt the algorithm iterates at the time of aggregation at the master. This iteration noise introduces a novel set of challenges that have not been previously studied in the literature. It needs to be treated differently from the well-studied setting of noise or corruption in the dataset itself. In this work, we first study the subspace learning problem in a federated over-the-air setting. Subspace learning involves computing the subspace spanned by the top $r$ singular vectors of a given matrix. We develop a federated over-the-air version of the power method (FedPM) and show that its iterates converge as long as (i) the channel noise is very small compared to the $r$-th singular value of the matrix; and (ii) the ratio between its $(r+1)$-th and $r$-th singular value is smaller than a constant less than one. The second important contribution of this work is to show how over-the-air FedPM can be used to obtain a provably accurate federated solution for subspace tracking in the presence of missing data.

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)