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: "sense scalable data acquisition from distributed sensors with guaranteed time coherence"
  Abstract: Data analysis in the Internet of Things (IoT) requires us to combine event streams from a huge amount of sensors. This combination (join) of events is usually based on the time stamps associated with the events. We address two challenges in environments which acquire and join events in the IoT: First, due to the growing number of sensors, we are facing the performance limits of central joins with respect to throughput, latency, and network utilization. Second, in the IoT, diverse sensor nodes are operated by different organizations and use different time synchronization techniques. Thus, events with the same timestamps are not necessarily recorded at the exact same time and joined data tuples have an unknown time incoherence. This can cause undetected failures, such as false correlations and wrong predictions. We present SENSE, a system for scalable data acquisition from distributed sensors. SENSE introduces time coherence measures as a fundamental data characteristic in addition to common time synchronization techniques. The time coherence of a data tuple is the time span in which all values contained in the tuple have been read from sensors. We explore concepts and algorithms to quantify and optimize time coherence and show that SENSE scales to thousands of sensors, operates efficiently under latency and coherence constraints, and adapts to changing network conditions.

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.DB(Databases)