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: "wideband spectrum sensing with sub nyquist sampling in cognitive radios"
  Abstract: Multi-rate asynchronous sub-Nyquist sampling (MASS) is proposed for wideband spectrum sensing. Corresponding spectral recovery conditions are derived and the probability of successful recovery is given. Compared to previous approaches, MASS offers lower sampling rate, and is an attractive approach for cognitive radio networks.

Connected through hyperedge HE-1 (induced by source paper "fast compressed power spectrum estimation towards a practical solution for wideband spectrum sensing"):
  with members:
    - "efficient spectrum availability information recovery for wideband dsa networks a weighted compressive sampling approach"
    - "sub nyquist sampling for power spectrum sensing in cognitive radios a unified approach"
    - "wideband spectrum sensing for cognitive radio networks a survey"
    - "xampling signal acquisition and processing in union of subspaces"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"

Connected through hyperedge HE-2 (induced by source paper "an active sequential xampling detector for spectrum sensing"):
  with members:
    - "utility maximizing sequential sensing over a finite horizon"
    - "converse bounds for noisy group testing with arbitrary measurement matrices"
    - "cross layer estimation and control for cognitive radio exploiting sparse network dynamics"
    - "info greedy sequential adaptive compressed sensing"
    - "sub nyquist sampling for power spectrum sensing in cognitive radios a unified approach"
    - "boolean compressed sensing and noisy group testing"
    - "on optimality of myopic policy for restless multi armed bandit problem an axiomatic approach"
    - "compressive and noncompressive power spectral density estimation from periodic nonuniform samples"

Connected through hyperedge HE-3 (induced by source paper "achieving autonomous compressive spectrum sensing for cognitive radios"):
  with members:
    - "wideband spectrum sensing for cognitive radio networks a survey"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "new and improved johnson lindenstrauss embeddings via the restricted isometry property"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"
    - "optimal multiband joint detection for spectrum sensing in cognitive radio networks"
    - "wideband spectrum sensing in cognitive radio networks"

Connected through hyperedge HE-4 (induced by source paper "frequency estimation of multiple sinusoids with three sub nyquist channels"):
  with members:
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"

Connected through hyperedge HE-5 (induced by source paper "frequency estimation of multiple sinusoids with sub nyquist sampling sequences"):
  with members:
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"

Connected through hyperedge HE-6 (induced by source paper "covariance based ofdm spectrum sensing with sub nyquist samples"):
  with members:
    - "xampling signal acquisition and processing in union of subspaces"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "wideband spectrum sensing at sub nyquist rates"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"

Connected through hyperedge HE-7 (induced by source paper "an algorithm of frequency estimation for multi channel coprime sampling"):
  with members:
    - "frequency estimation of multiple sinusoids with three sub nyquist channels"

Connected through hyperedge HE-8 (induced by source paper "joint frequency estimation with two sub nyquist sampling sequences"):
  with members:
    - "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)