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: "new outer bounds for the interference channel with unilateral source cooperation"
  Abstract: This paper studies the two-user interference channel with unilateral source cooperation, which consists of two source-destination pairs that share the same channel and where one full-duplex source can overhear the other source through a noisy in-band link. Novel outer bounds of the type 2R1 + R2 and R1 + 2R2 are developed for the class of injective semi-deterministic channels with independent noises at the different source-destination pairs. The bounds are then specialized to the Gaussian noise case. Interesting insights are provided about when these types of bounds are active, or in other words, when unilateral cooperation is too weak and leaves some system resources underutilized.

Connected through hyperedge HE-1 (induced by source paper "capacity bounds for a class of interference relay channels"):
  with members:
    - "mixed noisy network coding and cooperative unicasting in wireless networks"
    - "a new achievable scheme for interference relay channels"
    - "two birds and one stone gaussian interference channel with a shared out of band relay of limited rate"
    - "wireless network information flow a deterministic approach"
    - "incremental relaying for the gaussian interference channel with a degraded broadcasting relay"
    - "interference channels with source cooperation"
    - "the gaussian interference relay channel improved achievable rates and sum rate upperbounds using a potent relay"
    - "interference channels with destination cooperation"

Connected through hyperedge HE-2 (induced by source paper "the two user causal cognitive interference channel novel outer bounds and constant gap result for the symmetric gaussian noise channel in weak interference"):
  with members:
    - "on the capacity of the two user gaussian causal cognitive interference channel"
    - "capacity of all nine models of channel output feedback for the two user interference channel"
    - "an outer bound for the memoryless two user interference channel with general cooperation"
    - "on the capacity of interference channel with causal and noncausal generalized feedback at the cognitive transmitter"
    - "inner and outer bounds for the gaussian cognitive interference channel and new capacity results"
    - "dependence balance based outer bounds for gaussian networks with cooperation and feedback"
    - "interference channels with source cooperation"
    - "interference channels with destination cooperation"

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)