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: "on the achievable rate regions for state dependent gaussian interference channel"
  Abstract: In this paper, we study a general additive state-dependent Gaussian interference channel (ASD-GIC) where we consider two-user interference channel with two independent states known non-causally at both transmitters, but unknown to either of the receivers. An special case, where the additive states over the two links are the same is studied in [1], [2], in which it is shown that the gap between the achievable symmetric rate and the upper bound is less than 1/4 bit for the strong interference case. Here, we also consider the case where each channel state has unbounded variance [3], which is referred to as the strong interferences. We first obtain an outer bound on the capacity region. By utilizing lattice-based coding schemes, we obtain four achievable rate regions. Depend on noise variance and channel power constraint, achievable rate regions can coincide with the channel capacity region. For the symmetric model, the achievable sum-rate reaches to within 0.661 bit of the channel capacity for signal to noise ratio (SNR) greater than one.

Connected through hyperedge HE-1 (induced by source paper "multicoding schemes for interference channels"):
  with members:
    - "on the capacity of the two user gaussian causal cognitive interference channel"
    - "a comparison of superposition coding schemes"
    - "simultaneous nonunique decoding is rate optimal"
    - "wireless network information flow a deterministic approach"
    - "the state dependent semideterministic broadcast channel"
    - "interference channels with source cooperation"
    - "interference channels with destination cooperation"
    - "interference mitigation through limited transmitter cooperation"

Connected through hyperedge HE-2 (induced by source paper "parallel gaussian networks with a common state cognitive helper"):
  with members:
    - "secret key agreement with channel state information at the transmitter"
    - "the state dependent semideterministic broadcast channel"
    - "bounds on the capacity of the relay channel with noncausal state at source"
    - "capacity bounds for multiuser channels with non causal channel state information at the transmitters"
    - "distributed interference cancellation in multiple access channels"
    - "on the capacity of compound state dependent channels with states known at the transmitter"
    - "lattice strategies for the dirty multiple access channel"
    - "multiaccess channels with state known to some encoders and independent messages"

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)