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: "covariance based ofdm spectrum sensing with sub nyquist samples"
  Abstract: In this paper, we propose a feature-based method for spectrum sensing of OFDM signals from sub-Nyquist samples over a single band. We exploit the structure of the covariance matrix of OFDM signals to convert an underdetermined set of covariance-based equations to an overdetermined one. The statistical properties of sample covariance matrix are analyzed and then based on that an approximate Generalized Likelihood Ratio Test (GLRT) for detection of OFDM signals from sub-Nyquist samples is derived. The method is also extended to the frequency-selective channels. HighlightsA method for covariance-based spectrum sensing of OFDM signals from sub-Nyquist samples is introduced.The structure of the covariance matrix of OFDM signals is exploited for sub-Nyquist detection.The covariances between elements of sample covariance matrix are used to formulate an approximate Generalized Likelihood Ratio Test (GLRT).The method is extended to the frequency-selective channel case.

Connected through hyperedge HE-1 (induced by source paper "context aware decentralized invariant signaling for opportunistic communications"):
  with members:
    - "state of the art taxonomy and open issues on cognitive radio networks with noma"
    - "resource allocation and fairness in wireless powered cooperative cognitive radio networks"
    - "vandermonde subspace frequency division multiplexing for two tiered cognitive radio networks"
    - "distributed optimal beamformers for cognitive radios robust to channel uncertainties"
    - "enhanced intercell interference coordination challenges in heterogeneous networks"
    - "exploiting multi antennas for opportunistic spectrum sharing in cognitive radio networks"

Connected through hyperedge HE-2 (induced by source paper "compressive detection of random subspace signals"):
  with members:
    - "compressed beamforming in ultrasound imaging"
    - "sub nyquist radar via doppler focusing"
    - "measurement design for detecting sparse signals"
    - "xampling signal acquisition and processing in union of subspaces"
    - "measurement matrix design for compressive sensing based mimo radar"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"

Connected through hyperedge HE-3 (induced by source paper "compressive identification of active ofdm subcarriers in presence of timing offset"):
  with members:
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "wideband spectrum sensing at sub nyquist rates"

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)