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: "fractional interference alignment an interference alignment scheme for finite alphabet signals"
  Abstract: Interference Alignment (IA) is a transmission scheme which achieves 1/2 Degrees-of-Freedom (DoF) per transmit-antenna per user. The constraints imposed on the scheme are based on the linear receiver since conventional IA assumes Gaussian signaling. However, when the transmitters employ Finite Alphabet (FA) signaling, neither the conventional IA precoders nor the linear receiver are optimal structures. Therefore, a novel Fractional Interference Alignment (FIA) scheme is introduced when FA signals are used, where the alignment constraints are now based on the non-linear, minimum distance (MD) detector. Since DoF is defined only as signal-to-noise ratio tends to infinity, we introduce a new metric called SpAC (number of Symbols transmitted-per-transmit Antenna-per-Channel use) for analyzing the FIA scheme. The maximum SpAC is one, and the FIA achieves any value of SpAC in the range [0,1]. The key motivation for this work is that numerical simulations with FA signals and MD detector for fixed SpAC (=1/2, as in IA) over a set of optimization problems, like minimizing bit error rate or maximizing the mutual information, achieves a significantly better error rate performance when compared to the existing algorithms that minimize mean square error or maximize signal-to-interference plus noise ratio.

Connected through hyperedge HE-1 (induced by source paper "blind fractional interference alignment"):
  with members:
    - "approximate bayesian probabilistic data association aided iterative detection for mimo systems using arbitrary m ary modulation"
    - "exploiting channel correlations simple interference alignment schemes with no csit"
    - "ergodic interference alignment"

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)