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: "pilot contamination mitigation with reduced rf chains"
  Abstract: Massive multiple-input multiple-output (MIMO) communication is a promising technology for increasing spectral efficiency in wireless networks. Two of the main challenges massive MIMO systems face are degraded channel estimation accuracy due to pilot contamination and increase in computational load and hardware complexity due to the massive amount of antennas. In this paper, we focus on the problem of channel estimation in massive MIMO systems, while addressing these two challenges: We jointly design the pilot sequences to mitigate the effect of pilot contamination and propose an analog combiner which maps the high number of sensors to a low number of RF chains, thus reducing the computational and hardware cost. We consider a statistical model in which the channel covariance obeys a Kronecker structure. In particular, we treat two such cases, corresponding to fully- and partially-separable correlations. We prove that with these models, the analog combiner design can be done independently of the pilot sequences. Given the resulting combiner, we derive a closed-form expression for the optimal pilot sequences in the fully-separable case and suggest a greedy sum of ratio traces maximization (GSRTM) method for designing sub-optimal pilots in the partially-separable scenario. We demonstrate via simulations that our pilot design framework achieves lower mean squared error than the common pilot allocation framework previously considered for pilot contamination mitigation.

Connected through hyperedge HE-1 (induced by source paper "enhanced channel estimation in massive mimo via coordinated pilot design"):
  with members:
    - "fractional programming for communication systems part i power control and beamforming"
    - "optimization of mimo device to device networks via matrix fractional programming a minorization maximization approach"
    - "a successive optimization approach to pilot design for multi cell massive mimo systems"
    - "joint pilot design and uplink power allocation in multi cell massive mimo systems"
    - "smart pilot assignment for massive mimo"
    - "pilot beam pattern design for channel estimation in massive mimo systems"
    - "massive mimo for next generation wireless systems"
    - "blind pilot decontamination"

Connected through hyperedge HE-2 (induced by source paper "power control for massive mimo systems with nonorthogonal pilots"):
  with members:
    - "large scale fading decoding in cellular massive mimo systems with spatially correlated channels"
    - "massive mimo has unlimited capacity"
    - "joint pilot design and uplink power allocation in multi cell massive mimo systems"
    - "optimal pilot and payload power control in single cell massive mimo systems"
    - "a parallel decomposition method for nonconvex stochastic multi agent optimization problems"
    - "distributed massive mimo in cellular networks impact of imperfect hardware and number of oscillators"

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)