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: "unlocking energy neutrality in energy harvesting wireless sensor networks an approach based on distributed compressed sensing"
  Abstract: This paper advocates the use of the emerging distributed compressed sensing (DCS) paradigm to deploy energy harvesting (EH) wireless sensor networks (WSN) with practical network lifetime and data gathering rates that are substantially higher than the state-of-the-art. The basis of our work is a centralized EH WSN architecture where the sensors convey data to a fusion center, using stylized models that capture the fact that the signals collected by different nodes can exhibit correlation and that the energy harvested by different nodes can also exhibit some degree of correlation. Via the probability of incorrect data reconstruction, we characterize the performance of both a compressive sensing (CS) and a DCS based approach to data acquisition and reconstruction. Moreover, we perform an in-depth comparison of the proposed DCS based approach against a state-of-the-art distributed source coding (DSC) system in terms of decoded data distortion versus harvested energy. These performance characterizations and comparisons embody the effect of various system phenomena and parameters such as signal correlation, EH correlation, network size, and energy availability level. Our results unveil that, for an EH WSN consisting of eight SNs with our simple signal correlation and EH models, a target probability of incorrect reconstruction of $10^{-2}$, and under the same EH capability as CS, the proposed approach allows for a six-fold increase in data gathering capability with respect to the baseline CS-based approach. Moreover, under the same energy harvested level, the proposed solution offers a substantial reduction of the mean-squared error distortion (up to 66.67\%) with respect to the state-of-the-art DSC system.

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)