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: "sub nyquist sampling bridging theory and practice"
  Abstract: Sampling theory encompasses all aspects related to the conversion of continuous-time signals to discrete streams of numbers. The famous Shannon-Nyquist theorem has become a landmark in the development of digital signal processing. In modern applications, an increasingly number of functions is being pushed forward to sophisticated software algorithms, leaving only those delicate finely-tuned tasks for the circuit level. #R##N#In this paper, we review sampling strategies which target reduction of the ADC rate below Nyquist. Our survey covers classic works from the early 50's of the previous century through recent publications from the past several years. The prime focus is bridging theory and practice, that is to pinpoint the potential of sub-Nyquist strategies to emerge from the math to the hardware. In that spirit, we integrate contemporary theoretical viewpoints, which study signal modeling in a union of subspaces, together with a taste of practical aspects, namely how the avant-garde modalities boil down to concrete signal processing systems. Our hope is that this presentation style will attract the interest of both researchers and engineers in the hope of promoting the sub-Nyquist premise into practical applications, and encouraging further research into this exciting new frontier.

Connected through hyperedge HE-1 (induced by source paper "on recoverability of finite traces of square summable sequences"):
  with members:
    - "on exact and optimal recovering of missing values for sequences"

Connected through hyperedge HE-2 (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"
    - "xampling signal acquisition and processing in union of subspaces"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "wideband spectrum sensing at sub nyquist rates"

Connected through hyperedge HE-3 (induced by source paper "sub nyquist sar via fourier domain range doppler processing"):
  with members:
    - "fast compressed sensing sar imaging based on approximated observation"
    - "recovery of sparse matrices via matrix sketching"
    - "fourier domain beamforming the path to compressed ultrasound imaging"
    - "xampling at the rate of innovation"
    - "compressed beamforming in ultrasound imaging"
    - "sub nyquist radar via doppler focusing"
    - "multichannel sampling of pulse streams at the rate of innovation"

Connected through hyperedge HE-4 (induced by source paper "sub nyquist cyclostationary detection for cognitive radio"):
  with members:
    - "sub nyquist sampling for power spectrum sensing in cognitive radios a unified approach"
    - "noise folding in compressed sensing"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"

Connected through hyperedge HE-5 (induced by source paper "cascade compressed carrier and doa estimation"):
  with members:
    - "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"

Connected through hyperedge HE-6 (induced by source paper "channel capacity under sub nyquist nonuniform sampling"):
  with members:
    - "minimax capacity loss under sub nyquist universal sampling"
    - "shannon meets nyquist capacity of sampled gaussian channels"
    - "channel capacity under general nonuniform sampling"
    - "multichannel sampling of pulse streams at the rate of innovation"
    - "optimal phase transitions in compressed sensing"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"
    - "increased capacity per unit cost by oversampling"

Connected through hyperedge HE-7 (induced by source paper "minimax capacity loss under sub nyquist universal sampling"):
  with members:
    - "distortion rate function of sub nyquist sampled gaussian sources"
    - "sampling rate distortion"
    - "backing off from infinity performance bounds via concentration of spectral measure for random mimo channels"
    - "channel capacity under sub nyquist nonuniform sampling"
    - "shannon meets nyquist capacity of sampled gaussian channels"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"
    - "living at the edge a large deviations approach to the outage mimo capacity"
    - "multichannel sampling of pulse streams at the rate of innovation"

Connected through hyperedge HE-8 (induced by source paper "channel capacity under general nonuniform sampling"):
  with members:
    - "channel capacity under sub nyquist nonuniform sampling"
    - "shannon meets nyquist capacity of sampled gaussian channels"
    - "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)