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 "double detector for sparse signal detection from one bit compressed sensing measurements"):
  Abstract: This letter presents the sparse vector signal detection from one bit compressed sensing measurements, in contrast to the previous works that deal with scalar signal detection. Available results are extended to the vector case and the generalized likelihood ratio test (GLRT) detector and the optimal quantizer design are obtained. A double-detector scheme is introduced, in which a sensor level threshold detector is integrated into network level GLRT to improve the performance. The detection criteria of oracle and clairvoyant detectors are also derived. Simulation results show that with careful design of the threshold detector, the overall detection performance of double-detector scheme would be better than the sign-GLRT proposed in [J. Fang  et al.,  “One-bit quantizer design for multisensor GLRT fusion,”  IEEE Signal Process. Lett. , vol. 20, no. 3, pp. 257–260, Mar. 2013] and close to oracle and clairvoyant detectors. The proposed detector is applied to spectrum sensing and the results are near the well-known energy detector, which uses the real valued data, while the proposed detector only uses the sign of the data.

Includes members:
  Member "amplitude aided 1 bit compressive sensing over noisy wireless sensor networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fusion based cooperative support identification for compressive networked sensing")
  Member "one bit decentralized detection with a rao test for multisensor fusion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "asymptotic signal detection rates with 1 bit array measurements")
  Member "optimal precoding design and power allocation for decentralized detection of deterministic signals":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "universal collaboration strategies for signal detection a sparse learning approach")
  Member "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "serial quantization for sparse time sequences")

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)