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: "distributed data collection and storage algorithms for collaborative learning vision sensor devices with applications to pilgrimage"
  Abstract: This work presents novel distributed data collection systems and storage algorithms for collaborative learning wireless sensor networks (WSNs). In a large WSN, consider $n$ collaborative sensor devices distributed randomly to acquire information and learn about a certain field. Such sensors have less power, small bandwidth, and short memory, and they might disappear from the network after certain time of operations. The goal of this work is to design efficient strategies to learn about the field by collecting sensed data from these $n$ sensors with less computational overhead and efficient storage encoding operations. #R##N#In this data collection system, we propose two distributed data storage algorithms (DSA's) to solve this problem with the means of network flooding and connectivity among sensor devices. In the first algorithm denoted, DSA-I, it's assumed that the total number of nodes is known for each node in the network. We show that this algorithm is efficient in terms of the encoding/decoding operations. Furthermore, every node uses network flooding to disseminate its data throughout the network using mixing time approximately O(n). In the second algorithm denoted, DSA-II, it's assumed that the total number of nodes is not known for each learning sensor, hence dissemination of the data does not depend on the value of $n$. In this case we show that the encoding operations take $O(C\mu^2)$, where $\mu$ is the mean degree of the network graph and $C$ is a system parameter. Performance of these two algorithms match the derived theoretical results. Finally, we show how to deploy these algorithms for monitoring and measuring certain phenomenons in American-made camp tents located in Minna field in south-east side of Makkah.

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.NI(Networking and Internet Architecture)