human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "wideband spectrum sensing with sub nyquist sampling in cognitive radios":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fast compressed power spectrum estimation towards a practical solution for wideband spectrum sensing")
  Member "xampling signal acquisition and processing in union of subspaces":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fast compressed power spectrum estimation towards a practical solution for wideband spectrum sensing")
  Member "beyond nyquist efficient sampling of sparse bandlimited signals":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "secure and efficient compressed sensing based encryption with sparse matrices")
  Member "wideband spectrum sensing at sub nyquist rates":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "analog to digital cognitive radio sampling detection and hardware")
  Member "from theory to practice sub nyquist sampling of sparse wideband analog signals":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "image guided depth sampling and reconstruction")

Question: Please tell me which class the source paper 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)