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 "generalized rao test for decentralized detection of an uncooperative target"):
  Abstract: We tackle distributed detection of a noncooperative target with a wireless sensor network. When the target is present, sensors observe an (unknown) deterministic signal with attenuation depending on the distance between the sensor and the (unknown) target positions, embedded in symmetric and unimodal noise. The fusion center receives quantized sensor observations through error-prone binary symmetric channels and is in charge of performing a more-accurate global decision. The resulting problem is a two-sided parameter testing with nuisance parameters (i.e., the target position) present only under the alternative hypothesis. After introducing the generalized likelihood ratio test for the problem, we develop a novel fusion rule corresponding to a generalized Rao test, based on Davies’ framework, to reduce the computational complexity. Also, a rationale for threshold-optimization is proposed and confirmed by simulations. Finally, the aforementioned rules are compared in terms of performance and computational complexity.

Includes members:
  Member "distributed detection of a non cooperative target via generalized locally optimum approaches":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multisensor sequential change detection with unknown change propagation pattern")
  Member "decision fusion with unknown sensor detection probability":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rician mimo channel and jamming aware decision fusion")
  Member "one bit decentralized detection with a rao test for multisensor fusion":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "asymptotic signal detection rates with 1 bit array measurements")
  Member "optimality of received energy in decision fusion over rayleigh fading diversity mac with non identical sensors":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "decentralized hypothesis testing in energy harvesting wireless sensor networks")

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)