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: "decision fusion with unknown sensor detection probability"
  Abstract: In this letter we study the problem of channel-aware decision fusion when the sensor detection probability is not known at the decision fusion center. Several alternatives proposed in the literature are compared and new fusion rules (namely “ideal sensors” and “locally-optimum detection”) are proposed, showing attractive performance and linear complexity. Simulations are provided to compare the performance of the aforementioned rules.

Connected through hyperedge HE-1 (induced by source paper "rician mimo channel and jamming aware decision fusion"):
  with members:
    - "massive mimo for wireless sensing with a coherent multiple access channel"
    - "performance analysis and design of maximum ratio combining in channel aware mimo decision fusion"
    - "optimality of received energy in decision fusion over rayleigh fading diversity mac with non identical sensors"
    - "optimal power allocation for distributed detection over mimo channels in wireless sensor networks"

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"
    - "one bit decentralized detection with a rao test for multisensor fusion"
    - "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 "distributed detection of a non cooperative target via generalized locally optimum approaches"):
  with members:
    - "one bit decentralized detection with a rao test for multisensor fusion"

Connected through hyperedge HE-4 (induced by source paper "ir uwb detection and fusion strategies using multiple detector types"):
  with members:
    - "cooperative localization by dual foot mounted inertial sensors and inter agent ranging"

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)