human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "on the optimality of treating interference as noise compound interference networks"):
  Abstract: In a K-user Gaussian interference channel, it has been shown by Geng et al. that if for each user the desired signal strength is no less than the sum of the strengths of the strongest interference from this user and the strongest interference to this user (all values in dB scale), then power control and treating interference as noise (TIN) is optimal from the perspective of generalized degrees of freedom (GDoF) and achieves the entire channel capacity region to within a constant gap. In this work, we generalize the optimality of TIN to compound networks. We show that for a K-user compound Gaussian interference channel, if in every possible state for each receiver, the channel always satisfies the TIN-optimality condition identified by Geng et al., then the GDoF region of the compound channel is the intersection of the GDoF regions of all possible network realizations, which is achievable by power control and TIN. Furthermore, we demonstrate that for a general K-user compound interference channel, regardless of the number of states of each receiver, we can always construct a counterpart K-user regular interference channel that has the same TIN region as the original compound channel. The regular interference channel has only one state for each receiver, which may be different from all of the original states. Solving the GDoF-based power control problem for the compound channel is equivalent to solving the same problem in its regular counterpart. Exploring the power control problem further we develop a centralized power control scheme for K-user compound interference channels, to achieve all the Pareto optimal GDoF tuples. Finally, based on this scheme, we devise an iterative power control algorithm which requires at most K updates to obtain the globally optimal power allocation for any feasible GDoF tuple.

Includes members:
  Member "on the optimality of treating interference as noise a combinatorial optimization perspective":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimality of treating interference as noise a combinatorial perspective")
  Member "on the optimality of treating interference as noise general message sets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards an extremal network theory robust gdof gain of transmitter cooperation over tin")
  Member "on the optimality of treating interference as noise":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimality of simple layered superposition coding in the 3 user miso bc with finite precision csit")
  Member "index coding an interference alignment perspective":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "conditional disclosure of secrets a noise and signal alignment approach")
  Member "achievable and crystallized rate regions of the interference channel with interference as noise":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "treating interference as noise in cellular networks a stochastic geometry approach")
  Member "wireless network information flow a deterministic approach":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "best relay selection in gaussian half duplex diamond networks")
  Member "capacity regions and sum rate capacities of vector gaussian interference channels":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "signal processing and optimal resource allocation for the interference channel")
  Member "on the degrees of freedom of the compound mimo broadcast channels with finite states":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "optimality of simple layered superposition coding in the 3 user miso bc with finite precision csit")

Question: Please tell me which class the source paper 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)