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: "expected rip conditioning of the modulated wideband converter"
  Abstract: The sensing matrix of a compressive system impacts the stability of the associated sparse recovery problem. In this paper, we study the sensing matrix of the modulated wideband converter, a recently proposed system for sub-Nyquist sampling of analog sparse signals. Attempting to quantify the conditioning of the converter sensing matrix with existing approaches leads to unreasonable rate requirements, due to the relatively small size of this matrix. We propose a new conditioning criterion, named the expected restricted isometry property, and derive theoretical guarantees for the converter to satisfy this property. We then show that applying these conditions to popular binary sequences, such as maximal codes or Gold codes, leads to practical rate requirements.

Connected through hyperedge HE-1 (induced by source paper "analog to digital cognitive radio sampling detection and hardware"):
  with members:
    - "sub nyquist cyclostationary detection for cognitive radio"
    - "cascade compressed carrier and doa estimation"
    - "sub nyquist sampling for power spectrum sensing in cognitive radios a unified approach"
    - "compressive and noncompressive power spectral density estimation from periodic nonuniform samples"
    - "noise folding in compressed sensing"
    - "sub nyquist sampling bridging theory and practice"
    - "xampling signal acquisition and processing in union of subspaces"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"

Connected through hyperedge HE-2 (induced by source paper "sub nyquist radar principles and prototypes"):
  with members:
    - "analog to digital cognitive radio sampling detection and hardware"
    - "recovery of sparse matrices via matrix sketching"

Connected through hyperedge HE-3 (induced by source paper "spectrum sharing radar coexistence via xampling"):
  with members:
    - "analog to digital cognitive radio sampling detection and hardware"
    - "performance of time delay estimation in a cognitive radar"
    - "sub nyquist cyclostationary detection for cognitive radio"
    - "sub nyquist channel estimation over ieee 802 11ad link"
    - "on the co existence of td lte and radar over 3 5 ghz band an experimental study"
    - "spectrum sharing between a surveillance radar and secondary wi fi networks"
    - "recursive recovery of sparse signal sequences from compressive measurements a review"
    - "sub nyquist sampling for power spectrum sensing in cognitive radios a unified approach"

Connected through hyperedge HE-4 (induced by source paper "distributed compressed wideband sensing in cognitive radio sensor networks"):
  with members:
    - "energy efficient joint source and channel sensing in cognitive radio sensor networks"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"

Connected through hyperedge HE-5 (induced by source paper "on u statistics and compressed sensing i non asymptotic average case analysis"):
  with members:
    - "improved bounds on restricted isometry constants for gaussian matrices"
    - "compressed sensing how sharp is the restricted isometry property"
    - "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property"
    - "average case analysis of multichannel sparse recovery using convex relaxation"

Connected through hyperedge HE-6 (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"
    - "xampling in ultrasound imaging"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "xampling signal acquisition and processing in union of subspaces"):
  with members:
    - "innovation rate sampling of pulse streams with application to ultrasound imaging"
    - "identification of parametric underspread linear systems and super resolution radar"
    - "xampling compressed sensing of analog signals"
    - "multichannel sampling of pulse streams at the rate of innovation"
    - "recovering signals from lowpass data"
    - "sub nyquist sampling of short pulses"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"

Connected through hyperedge HE-8 (induced by source paper "sub nyquist sampling bridging theory and practice"):
  with members:
    - "performance bounds and design criteria for estimating finite rate of innovation signals"
    - "innovation rate sampling of pulse streams with application to ultrasound imaging"
    - "identification of parametric underspread linear systems and super resolution radar"
    - "multichannel sampling of pulse streams at the rate of innovation"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "wideband spectrum sensing at sub nyquist rates"
    - "time delay estimation from low rate samples a union of subspaces approach"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"

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)