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 "blind fractional interference alignment"):
  Abstract: Fractional Interference Alignment (FIA) is a transmission scheme which achieves any value between [0,1] for the Symbols transmitted per Antenna per Channel use (SpAC). FIA was designed in [1] specifically for Finite Alphabet (FA) signals, under the constraint that the Minimum Distance (MD) detector is used at all the receivers. Similar to classical interference alignment, the FIA precoder also needs perfect channel state information at all the transmitters (CSIT). In this work, a novel Blind Fractional Interference Alignment (B-FIA) scheme is introduced, where the basic assumption is that CSIT is not available. We consider two popular channel models, namely: Broadcast channel, and Interference channel. For these two channel models, the maximum achievable value of SpAC satisfying the constraints of the MD detector is obtained, but with no CSIT, and also a precoder design is provided to obtain any value of SpAC in the achievable range. #R##N#Further, the precoder structure provided has one distinct advantage: interference channel state information at the receiver (I-CSIR) is not needed, when all the transmitters and receivers are equipped with one antenna each. When two or more antennas are used at both ends, I-CSIR must be available to obtain the maximum achievable value of SpAC. The receiver designs for both the Minimum Distance and the Maximum Likelihood (ML) decoders are discussed, where the interference statistics is estimated from the received signal samples. Simulation results of the B-FIA show that the ML decoder with estimated statistics achieves a significantly better error rate performance when compared to the MD decoder with known statistics, since the MD decoder assumes the interference plus noise term as colored Gaussian noise.

Includes members:
  Member "approximate bayesian probabilistic data association aided iterative detection for mimo systems using arbitrary m ary modulation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a belief propagation based framework for soft multiple symbol differential detection")
  Member "fractional interference alignment an interference alignment scheme for finite alphabet signals":
    also connected through other hyperedges:
      (none)
  Member "exploiting channel correlations simple interference alignment schemes with no csit":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "achievable dof delay trade offs for the k user mimo interference channel with delayed csit")
  Member "ergodic interference alignment":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the interference channel revisited aligning interference by adjusting receive antenna separation")

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)