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: "robust iterative interference alignment for cellular networks with limited feedback"
  Abstract: In theory, coordinated multipoint transmission (CoMP) promises vast gains in spectral efficiency. However, industrial field trials show rather disappointing throughput gains, whereby the major limiting factor is proper sharing of channel state information. Many recent papers have considered this so-called limited feedback problem in the context of CoMP, usually taking the following assumptions, namely, infinite SNR regime, no user selection, and ideal link adaptation, rendering the analysis too optimistic. In this paper, we make a step forward toward a more realistic assessment of the limited feedback problem by introducing an improved metric for the performance evaluation, which better captures the throughput degradation. We find the relevant scaling laws (lower and upper bounds) and show that they are different from existing ones. Moreover, we provide a robust iterative interference alignment algorithm and corresponding feedback strategies achieving the obtained scaling laws. The main idea is that, instead of sending the complete channel matrix, each user fixes a receive filter and feeds back a quantized version of the effective channel. Finally, we underline our findings with simulations for the proposed system.

Connected through hyperedge HE-1 (induced by source paper "performance limits of compressive sensing channel estimation in dense cloud ran"):
  with members:
    - "recent advances in cloud radio access networks system architectures key techniques and open issues"
    - "sparse signal processing concepts for efficient 5g system design"
    - "dynamic nested clustering for parallel phy layer processing in cloud rans"
    - "exact performance analysis of the oracle receiver for compressed sensing reconstruction"

Connected through hyperedge HE-2 (induced by source paper "sparse signal processing concepts for efficient 5g system design"):
  with members:
    - "self calibration and biconvex compressive sensing"
    - "characterization of coded random access with compressive sensing based multi user detection"
    - "the achievable performance of convex demixing"
    - "a stability result for sparse convolutions"
    - "fundamentals of throughput maximization with random arrivals for m2m communications"
    - "five disruptive technology directions for 5g"
    - "the papr problem in ofdm transmission new directions for a long lasting problem"
    - "simultaneously structured models with application to sparse and low rank matrices"

Connected through hyperedge HE-3 (induced by source paper "compressive rate estimation with applications to device to device communications"):
  with members:
    - "sparse model uncertainties in compressed sensing with application to convolutions and sporadic communication"
    - "spectrum sharing scheme between cellular users and ad hoc device to device users"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "benchmarking practical rrm algorithms for d2d communications in lte advanced"
    - "power control for d2d underlaid cellular networks modeling algorithms and analysis"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "network mimo with linear zero forcing beamforming large system analysis impact of channel estimation and reduced complexity scheduling"
    - "robust analog function computation via wireless multiple access channels"

Connected through hyperedge HE-4 (induced by source paper "interference alignment with incomplete csit sharing"):
  with members:
    - "the feasibility conditions for interference alignment in mimo networks"
    - "csi feedback reduction for mimo interference alignment"
    - "on the degrees of freedom achievable through interference alignment in a mimo interference channel"
    - "feedback topology designs for interference alignment in mimo interference channels"
    - "adaptive beamforming in interference networks via bi directional training"
    - "user partitioning for less overhead in mimo interference channels"
    - "interference alignment as a rank constrained rank minimization"
    - "on feasibility of interference alignment in mimo interference networks"

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)