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: "large scale estimation in cyberphysical systems using streaming data a case study with smartphone traces"
  Abstract: Controlling and analyzing cyberphysical and robotics systems is increasingly becoming a Big Data challenge. Pushing this data to, and processing in the cloud is more efficient than on-board processing. However, current cloud-based solutions are not suitable for the latency requirements of these applications. We present a new concept, Discretized Streams or D-Streams, that enables massively scalable computations on streaming data with latencies as short as a second. We experiment with an implementation of D-Streams on top of the Spark computing framework. We demonstrate the usefulness of this concept with a novel algorithm to estimate vehicular traffic in urban networks. Our online EM algorithm can estimate traffic on a very large city network (the San Francisco Bay Area) by processing tens of thousands of observations per second, with a latency of a few seconds. Note to Practitioners This work was driven by the need to estimate vehicular traffic at a large scale, in an online setting, using commodity hardware. Machine Learning algorithms combined with streaming data are not new, but it still requires deep expertise both in Machine Learning and in Computer Systems to achieve large scale computations in a tractable manner. The Streaming Spark project aims at providing an interface that abstracts out all the technical details of the computation platform (cloud, HPC, workstation, etc.). As shown in this work, Streaming Spark is suitable for imple- menting and calibrating non-trivial algorithms on a large cluster, and provides an intuitive yet powerful programming interface. The readers are invited to refer to the source code referred in this article for more examples. This article presents algorithms to sample and compute densities for Gamma random variables restricted to a hyperplane (i.e. distributions of the form Tij P j jTj = d with Tj independant Gamma distributions). It is common in this case to use Gaussian random variables because of closed form solutions to solve. If one considers positive valued distributions with heavy tails, our formulas using gamma distributions may be more suitable.

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.RO(Robotics)