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 gaussian multiple access channels with constant modulus signaling"
  Abstract: A distributed detection scheme where the sensors transmit with constant modulus signals over a Gaussian multiple access channel is considered. The deflection coefficient of the proposed scheme is shown to depend on the characteristic function of the sensing noise and the error exponent for the system is derived using large deviation theory. Optimization of the deflection coefficient and error exponent are considered with respect to a transmission phase parameter for a variety of sensing noise distributions including impulsive ones. The proposed scheme is also favorably compared with existing amplify-and-forward and detect-and-forward schemes. The effect of fading is shown to be detrimental to the detection performance through a reduction in the deflection coefficient depending on the fading statistics. Simulations corroborate that the deflection coefficient and error exponent can be effectively used to optimize the error probability for a wide variety of sensing noise distributions.

Connected through hyperedge HE-1 (induced by source paper "nomographic functions efficient computation in clustered gaussian sensor networks"):
  with members:
    - "interactive computation of type threshold functions in collocated broadcast superposition networks"
    - "computation over mismatched channels"
    - "cooperative compute and forward"
    - "robust analog function computation via wireless multiple access channels"
    - "compute and forward harnessing interference through structured codes"
    - "on the role of mmse estimation in approaching the information theoretic limits of linear gaussian channels shannon meets wiener"

Connected through hyperedge HE-2 (induced by source paper "distributed estimation and detection with bounded transmissions over gaussian multiple access channels"):
  with members:
    - "non linear distributed average consensus using bounded transmissions"
    - "robust analog function computation via wireless multiple access channels"
    - "distributed snr estimation using constant modulus signaling over gaussian multiple access channels"
    - "universal distributed estimation over multiple access channels with constant modulus signaling"
    - "estimation diversity and energy efficiency in distributed sensing"

Connected through hyperedge HE-3 (induced by source paper "robust analog function computation via wireless multiple access channels"):
  with members:
    - "reliable physical layer network coding"
    - "network coding for computing cut set bounds"
    - "gossip algorithms for distributed signal processing"

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)