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: "distributed detection over fading macs with multiple antennas at the fusion center"
  Abstract: We consider a distributed detection problem over fading multiple-access channels. Sensors observe a phenomenon and transmit their observations to a fusion center using the amplify-and-forward scheme. The fusion center has multiple antennas and uses the transmissions from the sensors to run a detection algorithm. The channels are Ricean fading, and the sensors have no channel information. The performance is evaluated in terms of error exponent and compared with the AWGN channels case. The benefit of having multiple antennas at the fusion center is also quantified.

Connected through hyperedge HE-1 (induced by source paper "optimized sensor collaboration for estimation of temporally correlated parameters"):
  with members:
    - "network topology adaptation and interference coordination for energy saving in heterogeneous networks"
    - "large scale convex optimization for dense wireless cooperative networks"
    - "optimal power allocation for distributed blue estimation with linear spatial collaboration"
    - "distributed estimation and detection with bounded transmissions over gaussian multiple access channels"
    - "controlled collaboration for linear coherent estimation in wireless sensor networks"
    - "optimal identical binary quantizer design for distributed estimation"
    - "on linear coherent estimation with spatial collaboration"
    - "estimation diversity and energy efficiency in distributed sensing"

Connected through hyperedge HE-2 (induced by source paper "estimation in phase shift and forward wireless sensor networks"):
  with members:
    - "phase only analog encoding for a multi antenna fusion center"
    - "the ergodic capacity of phase fading interference networks"
    - "on scaling laws of diversity schemes in decentralized estimation"
    - "universal distributed estimation over multiple access channels with constant modulus signaling"
    - "optimal spectrum sharing in mimo cognitive radio networks via semidefinite programming"
    - "estimation diversity and energy efficiency in distributed sensing"

Connected through hyperedge HE-3 (induced by source paper "optimality of received energy in decision fusion over rayleigh fading diversity mac with non identical sensors"):
  with members:
    - "optimal power allocation for distributed detection over mimo channels in wireless sensor networks"

Connected through hyperedge HE-4 (induced by source paper "phase only analog encoding for a multi antenna fusion center"):
  with members:
    - "on scaling laws of diversity schemes in decentralized estimation"
    - "optimal spectrum sharing in mimo cognitive radio networks via semidefinite programming"
    - "estimation diversity and energy efficiency in distributed sensing"

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)