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: "super resolution radar imaging via convex optimization"
  Abstract: A radar system emits probing signals and records the reflections. Estimating the relative angles, delays, and Doppler shifts from the received signals allows to determine the locations and velocities of objects. However, due to practical constraints, the probing signals have finite bandwidth B, the received signals are observed over a finite time interval of length T only, and a radar typically has only one or a few transmit and receive antennas. These constraints fundamentally limit the resolution up to which objects can be distinguished. Specifically, a radar can not distinguish objects with delay and Doppler shifts much closer than 1/B and 1/T, respectively, and a radar system with N_T transmit and N_R receive antennas cannot distinguish objects with angels closer than 1/(N_T N_R). As a consequence, the delay, Doppler, and angular resolution of standard radars is proportional to 1/B and 1/T, and 1/(N_T N_R). In this chapter, we show that the continuous angle-delay-Doppler triplets and the corresponding attenuation factors can be resolved at much finer resolution, using ideas from compressive sensing. Specifically, provided the angle-delay-Doppler triplets are separated either by factors proportional to 1/(N_T N_R-1) in angle, 1/B in delay, or 1/T in Doppler direction, they can be recovered a significantly smaller scale or higher resolution.

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)