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: "recovering signals from lowpass data"
  Abstract: The problem of recovering a signal from its low frequency components occurs often in practical applications due to the lowpass behavior of many physical systems. Here, we study in detail conditions under which a signal can be determined from its low-frequency content. We focus on signals in shift-invariant spaces generated by multiple generators. For these signals, we derive necessary conditions on the cutoff frequency of the lowpass filter as well as necessary and sufficient conditions on the generators such that signal recovery is possible. When the lowpass content is not sufficient to determine the signal, we propose appropriate pre-processing that can improve the reconstruction ability. In particular, we show that modulating the signal with one or more mixing functions prior to lowpass filtering, can ensure the recovery of the signal in many cases, and reduces the necessary bandwidth of the filter.

Connected through hyperedge HE-1 (induced by source paper "system approximations and generalized measurements in modern sampling theory"):
  with members:
    - "signal and system approximation from general measurements"
    - "phase retrieval via structured modulations in paley wiener spaces"
    - "phaseless signal recovery in infinite dimensional spaces using structured modulations"
    - "phase retrieval from low rate samples"
    - "the papr problem in ofdm transmission new directions for a long lasting problem"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "phase retrieval via matrix completion"
    - "beyond bandlimited sampling nonlinearities smoothness and sparsity"

Connected through hyperedge HE-2 (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"
    - "sub nyquist sampling of short pulses"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "time delay estimation from low rate samples a union of subspaces approach"

Connected through hyperedge HE-3 (induced by source paper "multichannel sampling of pulse streams at the rate of innovation"):
  with members:
    - "innovation rate sampling of pulse streams with application to ultrasound imaging"
    - "xampling signal acquisition and processing in union of subspaces"
    - "sub nyquist sampling of short pulses"
    - "time delay estimation from low rate samples a union of subspaces approach"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"
    - "uncertainty relations for shift invariant analog signals"
    - "compressed sensing of analog signals in shift invariant spaces"
    - "expected rip conditioning of the modulated wideband converter"

Connected through hyperedge HE-4 (induced by source paper "sub nyquist sampling of short pulses"):
  with members:
    - "innovation rate sampling of pulse streams with application to ultrasound imaging"
    - "sampling and recovery of pulse streams"
    - "identification of parametric underspread linear systems and super resolution radar"
    - "multichannel sampling of pulse streams at the rate of innovation"
    - "xampling signal acquisition and processing in union of subspaces"
    - "time delay estimation from low rate samples a union of subspaces approach"
    - "rank awareness in joint sparse recovery"
    - "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)