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: "distributed mimo radar using compressive sampling"
  Abstract: A distributed MIMO radar is considered, in which the transmit and receive antennas belong to nodes of a small scale wireless network. The transmit waveforms could be uncorrelated, or correlated in order to achieve a desirable beampattern. The concept of compressive sampling is employed at the receive nodes in order to perform direction of arrival (DOA) estimation. According to the theory of compressive sampling, a signal that is sparse in some domain can be recovered based on far fewer samples than required by the Nyquist sampling theorem. The DOAs of targets form a sparse vector in the angle space, and therefore, compressive sampling can be applied for DOA estimation. The proposed approach achieves the superior resolution of MIMO radar with far fewer samples than other approaches. This is particularly useful in a distributed scenario, in which the results at each receive node need to be transmitted to a fusion center.

Connected through hyperedge HE-1 (induced by source paper "measurement matrix design for compressive sensing based mimo radar"):
  with members:
    - "cssf mimo radar low complexity compressive sensing based mimo radar that uses step frequency"
    - "target localization accuracy gain in mimo radar based systems"
    - "mimo radar using compressive sampling"
    - "compressive sensing for mimo radar"
    - "performance analysis for sparse support recovery"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "collaborative beamforming for distributed wireless ad hoc sensor networks"

Connected through hyperedge HE-2 (induced by source paper "cssf mimo radar low complexity compressive sensing based mimo radar that uses step frequency"):
  with members:
    - "mimo radar using compressive sampling"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "reduced complexity angle doppler range estimation for mimo radar that employs compressive sensing"
    - "step frequency radar with compressive sampling sfr cs"

Connected through hyperedge HE-3 (induced by source paper "mimo radar using compressive sampling"):
  with members:
    - "collaborative beamforming for distributed wireless ad hoc sensor networks"

Connected through hyperedge HE-4 (induced by source paper "reduced complexity angle doppler range estimation for mimo radar that employs compressive sensing"):
  with members:
    - "mimo radar using compressive sampling"

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)