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 "power allocation strategies and lattice based coding schemes for bi directional relaying"):
  Abstract: We consider a communication system where two transmitters wish to exchange information through a half-duplex relay in the middle. The channels between the transmitters and the relay have asymmetric channel gains. More specifically, the channels are assumed to be synchronized with complex inputs and complex fading coefficients with an average power constraint on the inputs to the channels. The noise at the receivers have the same power spectral density and are assumed to be white and Gaussian. We restrict our attention to transmission schemes where information from the two nodes are simultaneously sent to the relay during a medium access phase followed by a broadcast phase where the relay broadcasts information to both the nodes. An upper bound on the capacity for the two phase protocol under a sum power constraint on the transmit power from all the nodes is obtained as a solution to a convex optimization problem. We show that a scheme using channel inversion with lattice decoding can obtain a rate a small constant 0.09 bits from the upper bound at high signal-to-noise ratios. Numerical results show that the proposed scheme can perform very close to the upper bound.

Includes members:
  Member "joint physical layer coding and network coding for bi directional relaying":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "compute and forward relaying with ldpc codes over qpsk scheme")
  Member "achievable rate regions for bi directional relaying":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "resource allocation and outage analysis for an adaptive cognitive two way relay network")

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)