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: "snipe for memory limited pca from incomplete data"
  Abstract: The linear subspace model is pervasive in science and engineering and particularly in large datasets which are often incomplete due to missing measurements and privacy issues. Therefore, a critical problem in modeling is to develop algorithms for estimating a low-dimensional subspace model from incomplete data efficiently in terms of both computational complexity and memory storage. In this paper we study an algorithm that processes blocks of incomplete data to estimate the underlying subspace model. Our algorithm has a simple interpretation as optimizing the subspace to fit the observed data block but remain close to the previous estimate. We prove a linear rate of convergence for the algorithm and our rate holds with high probability.

Connected through hyperedge HE-1 (induced by source paper "what to expect when you are expecting on the grassmannian"):
  with members:
    - "intrinsic grassmann averages for online linear robust and nonlinear subspace learning"
    - "streaming principal component analysis from incomplete data"
    - "incoherence optimal matrix completion"
    - "local convergence of an algorithm for subspace identification from partial data"
    - "matrix completion from a few entries"

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.IT(Information Theory)