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: "one bit decentralized detection with a rao test for multisensor fusion"
  Abstract: In this letter, we propose the Rao test as a simpler alternative to the generalized likelihood ratio test (GLRT) for multisensor fusion. We consider sensors observing an unknown deterministic parameter with symmetric and unimodal noise. A decision fusion center (DFC) receives quantized sensor observations through error-prone binary symmetric channels and makes a global decision. We analyze the optimal quantizer thresholds and we study the performance of the Rao test in comparison to the GLRT. Also, a theoretical comparison is made and asymptotic performance is derived in a scenario with homogeneous sensors. All the results are confirmed through simulations.

Connected through hyperedge HE-1 (induced by source paper "asymptotic signal detection rates with 1 bit array measurements"):
  with members:
    - "throughput analysis of massive mimo uplink with low resolution adcs"
    - "double detector for sparse signal detection from one bit compressed sensing measurements"
    - "doa parameter estimation with 1 bit quantization bounds methods and the exponential replacement"
    - "low power wideband sensing for one bit quantized cognitive radio systems"
    - "near maximum likelihood detector and channel estimator for uplink multiuser massive mimo systems with one bit adcs"

Connected through hyperedge HE-2 (induced by source paper "generalized rao test for decentralized detection of an uncooperative target"):
  with members:
    - "distributed detection of a non cooperative target via generalized locally optimum approaches"
    - "decision fusion with unknown sensor detection probability"
    - "optimality of received energy in decision fusion over rayleigh fading diversity mac with non identical sensors"

Connected through hyperedge HE-3 (induced by source paper "double detector for sparse signal detection from one bit compressed sensing measurements"):
  with members:
    - "amplitude aided 1 bit compressive sensing over noisy wireless sensor networks"
    - "optimal precoding design and power allocation for decentralized detection of deterministic signals"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"

Connected through hyperedge HE-4 (induced by source paper "distributed detection of a non cooperative target via generalized locally optimum approaches"):
  with members:
    - "decision fusion with unknown sensor detection probability"

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)