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: "low power receiver front ends scaling laws and applications"
  Abstract: In this paper, we combine communication-theoretic laws with known, practically verified results from circuit theory. As a result, we obtain closed-form theoretical expressions linking fundamental system design and environment parameters with the power consumption of analog front ends for communication receivers. This collection of scaling laws and bounds is meant to serve as a theoretical reference for practical low power front end design. In one set of results, we first find that the front end power consumption scales at least as SNDR^3/2 if environment parameters (fading and blocker levels) are static. The obtained scaling law is subsequently used to derive relations between front end power consumption and several other important communication system parameters, namely, digital modulation constellation size, symbol error probability, error control coding gain and coding rate. Such relations, in turn, can be used when deciding which system design strategies to adopt for low-power applications. For example, if error control coding is employed, the most energy-efficient strategy for the entire receiver is to use codes with moderate coding gain and simple decoding algorithms, such as convolutional codes. In another collection of results, we find how front end power scales with environment parameters if the performance is kept constant. This yields bounds on average power reduction of receivers that adapt to the communication environment. For instance, if a receiver front end adapts to fading fluctuations while keeping the performance above some given minimum requirement, power can theoretically be reduced at least 20x compared to a non-adaptive front end.

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)