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: "xampling in ultrasound imaging"
  Abstract: Recent developments of new medical treatment techniques put challenging demands on ultrasound imaging#R##N#systems in terms of both image quality and raw data size. Traditional sampling methods result in very large#R##N#amounts of data, thus, increasing demands on processing hardware and limiting the flexibility in the postprocessing#R##N#stages.#R##N#In this paper, we apply Compressed Sensing (CS) techniques to analog ultrasound signals, following the recently#R##N#developed Xampling framework. The result is a system with significantly reduced sampling rates which, in turn,#R##N#means significantly reduced data size while maintaining the quality of the resulting images.

Connected through hyperedge HE-1 (induced by source paper "compressed beamforming in ultrasound imaging"):
  with members:
    - "xampling at the rate of innovation"
    - "innovation rate sampling of pulse streams with application to ultrasound imaging"
    - "multichannel sampling of pulse streams at the rate of innovation"
    - "xampling signal acquisition and processing in union of subspaces"

Connected through hyperedge HE-2 (induced by source paper "structured compressed sensing from theory to applications"):
  with members:
    - "performance bounds and design criteria for estimating finite rate of innovation signals"
    - "exploiting statistical dependencies in sparse representations for signal recovery"
    - "innovation rate sampling of pulse streams with application to ultrasound imaging"
    - "near oracle performance of greedy block sparse estimation techniques from noisy measurements"
    - "identification of parametric underspread linear systems and super resolution radar"
    - "multichannel sampling of pulse streams at the rate of innovation"
    - "noise folding in compressed sensing"
    - "general deviants an analysis of perturbations in compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "compressed beamforming applied to b mode ultrasound imaging"):
  with members:
    - "innovation rate sampling of pulse streams with application to ultrasound imaging"
    - "multichannel sampling of pulse streams at the rate of innovation"

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)