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: "achieving super resolution with redundant sensing"
  Abstract: Objective : A new technique is presented to enhance the precision of the analog-to-digital (AD) and digital-to-analog (DA) conversion, which are fundamental operations of many biomedical information processing systems. In practice, the precision of these operations is always bounded, first by the random mismatch error occurred during system implementation, and subsequently by the intrinsic quantization error determined by the system architecture itself.  Methods : Here, we derive a new mathematical interpretation of the previously proposed redundant sensing architecture that not only suppresses mismatch error but also allows achieving an effective resolution exceeding the system's intrinsic resolution, i.e., super-resolution (SR). SR is enabled by an endogenous property of redundant structures regarded as “code diffusion” where the references’ value spreads into the neighbor sample space as a result of mismatch error.  Results : Using Monte Carlo methods, we show a profound theoretical increase of 8–9 b effective resolution or 256–512× enhancement of precision on a 10-b device at 95% sample space.  Conclusion : The proposed SR mechanism can be applied to substantially improve the precision of various AD and DA conversion processes beyond the system resource constraints.  Significance : The concept opens the possibility for a wide range of applications in low-power fully integrated sensors and devices where the cost-accuracy tradeoff is inevitable. As a proof-of-concept demonstration, we point out an example where the proposed technique can be used to enhance the precision of an implantable neurostimulator design.

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)