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: "cognitive sub nyquist hardware prototype of a collocated mimo radar"
  Abstract: We present the design and hardware implementation of a radar prototype that demonstrates the principle of a sub-Nyquist collocated multiple-input multiple-output (MIMO) radar. The setup allows sampling in both spatial and spectral domains at rates much lower than dictated by the Nyquist sampling theorem. Our prototype realizes an X-band MIMO radar that can be configured to have a maximum of 8 transmit and 10 receive antenna elements. We use frequency division multiplexing (FDM) to achieve the orthogonality of MIMO waveforms and apply the Xampling framework for signal recovery. The prototype also implements a cognitive transmission scheme where each transmit waveform is restricted to those pre-determined subbands of the full signal bandwidth that the receiver samples and processes. Real-time experiments show reasonable recovery performance while operating as a 4x5 thinned random array wherein the combined spatial and spectral sampling factor reduction is 87.5% of that of a filled 8x10 array.

Connected through hyperedge HE-1 (induced by source paper "dictionary learning for adaptive gpr landmine classification"):
  with members:
    - "sub nyquist radar principles and prototypes"
    - "on learning with shift invariant structures"
    - "convolutional dictionary learning a comparative review and new algorithms"
    - "fast low rank shared dictionary learning for image classification"
    - "performance of time delay estimation in a cognitive radar"
    - "online dictionary learning aided target recognition in cognitive gpr"
    - "trainlets dictionary learning in high dimensions"
    - "fast r cnn"

Connected through hyperedge HE-2 (induced by source paper "performance of time delay estimation in a cognitive radar"):
  with members:
    - "xampling signal acquisition and processing in union of subspaces"
    - "time delay estimation in dispersed spectrum cognitive radio systems"
    - "a lower bound on the bayesian mse based on the optimal bias function"

Connected through hyperedge HE-3 (induced by source paper "sub nyquist channel estimation over ieee 802 11ad link"):
  with members:
    - "millimeter wave vehicular communication to support massive automotive sensing"
    - "channel estimation in broadband millimeter wave mimo systems with few bit adcs"

Connected through hyperedge HE-4 (induced by source paper "online dictionary learning aided target recognition in cognitive gpr"):
  with members:
    - "performance of time delay estimation in a cognitive radar"

Connected through hyperedge HE-5 (induced by source paper "summer sub nyquist mimo radar"):
  with members:
    - "mimo mc radar a mimo radar approach based on matrix completion"
    - "spatial compressive sensing for mimo radar"
    - "recovery of sparse matrices via matrix sketching"
    - "matrix completion in colocated mimo radar recoverability bounds theoretical guarantees"
    - "fourier domain beamforming the path to compressed ultrasound imaging"
    - "analysis of sparse mimo radar"
    - "compressed beamforming in ultrasound imaging"
    - "sub nyquist radar via doppler focusing"

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)