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: "uplink interference reduction in large scale antenna systems"
  Abstract: A massive MIMO system entails a large number (tens or hundreds) of base station antennas serving a much smaller number of terminals. These systems demonstrate large gains in spectral and energy efficiency compared with conventional MIMO technology. As the number of antennas grows, the performance of a massive MIMO system gets limited by the interference caused by pilot contamination. Earlier A. Ashikhmin and T. Marzetta proposed (under the name of Pilot Contamination Precoding) Large Scale Fading Precoding (LSFP) and Decoding (LSFD) based on limited cooperation between base stations. They showed that Zero-Forcing LSFP and LSFD eliminate pilot contamination entirely and lead to an infinite throughput as the number of antennas grows. #R##N#In this paper, we focus on the uplink and show that even in the case of a finite number of base station antennas, LSFD yields a very large performance gain. In particular, one of our algorithms gives a more than 140 fold increase in the 5% outage data transmission rate! We show that the performance can be improved further by optimizing the transmission powers of the users. Finally, we present decentralized LSFD that requires limited cooperation only between neighboring cells.

Connected through hyperedge HE-1 (induced by source paper "performance of multi cell massive mimo systems with interference decoding"):
  with members:
    - "large scale fading decoding in cellular massive mimo systems with spatially correlated channels"
    - "massive mimo has unlimited capacity"
    - "gaussian multiple access via compute and forward"
    - "joint power allocation and user association optimization for massive mimo systems"
    - "robust pilot decontamination based on joint angle and power domain discrimination"
    - "optimal achievable rates for interference networks with random codes"
    - "system architecture and key technologies for 5g heterogeneous cloud radio access networks"
    - "massive mimo for next generation wireless systems"

Connected through hyperedge HE-2 (induced by source paper "power control in cellular massive mimo with varying user activity a deep learning solution"):
  with members:
    - "sum spectral efficiency maximization in massive mimo systems benefits from deep learning"
    - "optimized power control for massive mimo with underlaid d2d communications"
    - "joint transmit and circuit power minimization in massive mimo with downlink sinr constraints when to turn on massive mimo"
    - "large scale fading decoding in cellular massive mimo systems with spatially correlated channels"
    - "learning to optimize training deep neural networks for interference management"
    - "model aided wireless artificial intelligence embedding expert knowledge in deep neural networks towards wireless systems optimization"
    - "residual dense network for image super resolution"
    - "an introduction to deep learning for the physical layer"

Connected through hyperedge HE-3 (induced by source paper "making cell free massive mimo competitive with mmse processing and centralized implementation"):
  with members:
    - "ubiquitous cell free massive mimo communications"
    - "large scale fading decoding in cellular massive mimo systems with spatially correlated channels"
    - "few bit csi acquisition for centralized cell free massive mimo with spatial correlation"
    - "can massive mimo support uplink intensive applications"
    - "on the performance of backhaul constrained cell free massive mimo with linear receivers"
    - "massive mimo has unlimited capacity"
    - "performance of cell free massive mimo systems with mmse and lsfd receivers"
    - "on the total energy efficiency of cell free massive mimo"

Connected through hyperedge HE-4 (induced by source paper "joint agc and receiver design for large scale mu mimo systems with coarsely quantized signals and c rans"):
  with members:
    - "throughput analysis of massive mimo uplink with low resolution adcs"
    - "branch and bound precoding for multiuser mimo systems with 1 bit quantization"
    - "widely linear precoding for large scale mimo with iqi algorithms and performance analysis"
    - "full duplex cloud radio access network stochastic design and analysis"
    - "iterative detection and decoding algorithms for mimo systems in block fading channels using ldpc codes"
    - "robust adaptive beamforming based on low rank and cross correlation techniques"
    - "cloud radio access network virtualizing wireless access for dense heterogeneous systems"
    - "adaptive reduced rank receive processing based on minimum symbol error rate criterion for large scale multiple antenna systems"

Connected through hyperedge HE-5 (induced by source paper "large scale fading decoding in cellular massive mimo systems with spatially correlated channels"):
  with members:
    - "two layer decoding in cellular massive mimo systems with spatial channel correlation"
    - "uplink massive mimo for channels with spatial correlation"
    - "massive mimo has unlimited capacity"
    - "performance of cell free massive mimo systems with mmse and lsfd receivers"
    - "smart pilot assignment for massive mimo"
    - "massive mimo with non ideal arbitrary arrays hardware scaling laws and circuit aware design"
    - "what will 5g be"
    - "massive mimo for maximal spectral efficiency how many users and pilots should be allocated"

Connected through hyperedge HE-6 (induced by source paper "two layer decoding in cellular massive mimo systems with spatial channel correlation"):
  with members:
    - "large scale fading decoding in cellular massive mimo systems with spatially correlated channels"
    - "uplink massive mimo for channels with spatial correlation"
    - "massive mimo has unlimited capacity"
    - "performance of cell free massive mimo systems with mmse and lsfd receivers"
    - "what will 5g be"
    - "massive mimo for maximal spectral efficiency how many users and pilots should be allocated"
    - "pilot contamination and precoding in multi cell tdd systems"

Connected through hyperedge HE-7 (induced by source paper "performance of cell free massive mimo with rician fading and phase shifts"):
  with members:
    - "a new look at cell free massive mimo making it practical with dynamic cooperation"
    - "two layer decoding in cellular massive mimo systems with spatial channel correlation"
    - "ubiquitous cell free massive mimo communications"
    - "large scale fading decoding in cellular massive mimo systems with spatially correlated channels"
    - "performance of cell free massive mimo systems with mmse and lsfd receivers"
    - "cell free massive mimo versus small cells"
    - "scalable synchronization and reciprocity calibration for distributed multiuser mimo"

Connected through hyperedge HE-8 (induced by source paper "a bilinear equalizer for massive mimo systems"):
  with members:
    - "massive mimo has unlimited capacity"
    - "massive mimo with imperfect channel covariance information"
    - "robust pilot decontamination based on joint angle and power domain discrimination"
    - "cell free massive mimo versus small cells"
    - "cell free massive mimo uniformly great service for everyone"
    - "low complexity polynomial channel estimation in large scale mimo with arbitrary statistics"
    - "massive mimo for maximal spectral efficiency how many users and pilots should be allocated"
    - "scaling up mimo opportunities and challenges with very large arrays"

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)