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 transmit strategy for the interference mimo relay channel in the low power regime"):
  Abstract: This paper studies the interference channel with two transmitters and two receivers in the presence of a MIMO relay in the low transmit power regime. A communication scheme combining block Markov encoding, beamforming, and Willems' backward decoding is used. With this scheme, we get an interference channel with channel gains dependent on the signal power. A power allocation for this scheme is proposed, and the achievable rate region with this power allocation is given. We show that, at low transmit powers, with equal power constraints at the relay and the transmitters, the interference channel with a MIMO relay achieves a sum rate that is linear in the power. This sum rate is determined by the channel setup. We also show that in the presence of abundant power at the relay, the transmit strategy is significantly simplified, and the MAC from the transmitters to the relay forms the bottle neck of the system from the sum rate point of view.

Includes members:
  Member "rate region frontiers for n user interference channel with interference as noise":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "full duplex radio for uplink downlink wireless access with spatially random nodes")
  Member "gaussian interference networks sum capacity in the low interference regime and new outer bounds on the capacity region":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "extended generalized dof optimality regime of treating interference as noise in the x channel")
  Member "capacity bounds for the gaussian interference channel":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "vector gaussian successive refinement with degraded side information")

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)