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: "an overview of signal processing techniques for millimeter wave mimo systems"
  Abstract: Communication at millimeter wave (mmWave) frequencies is defining a new era of wireless communication. The mmWave band offers higher bandwidth communication channels versus those presently used in commercial wireless systems. The applications of mmWave are immense: wireless local and personal area networks in the unlicensed band, 5G cellular systems, not to mention vehicular area networks, ad hoc networks, and wearables. Signal processing is critical for enabling the next generation of mmWave communication. Due to the use of large antenna arrays at the transmitter and receiver, combined with radio frequency and mixed signal power constraints, new multiple-input multiple-output (MIMO) communication signal processing techniques are needed. Because of the wide bandwidths, low complexity transceiver algorithms become important. There are opportunities to exploit techniques like compressed sensing for channel estimation and beamforming. This article provides an overview of signal processing challenges in mmWave wireless systems, with an emphasis on those faced by using MIMO communication at higher carrier frequencies.

Connected through hyperedge HE-1 (induced by source paper "energy efficient hybrid precoding design for integrated multicast unicast millimeter wave communications with swipt"):
  with members:
    - "hybrid precoding based millimeter wave massive mimo noma with simultaneous wireless information and power transfer"
    - "joint optimization of computation and communication power in multi user massive mimo systems"
    - "energy efficient transceiver design for hybrid sub array architecture mimo systems"
    - "energy efficient power allocation in millimeter wave massive mimo with non orthogonal multiple access"
    - "joint base station clustering and beamforming for non orthogonal multicast and unicast transmission with backhaul constraints"
    - "joint beamforming and broadcasting in massive mimo"
    - "on the spectral efficiency and security enhancements of noma assisted multicast unicast streaming"
    - "energy efficiency optimization of 5g radio frequency chain systems"

Connected through hyperedge HE-2 (induced by source paper "deep reinforcement learning for 5g networks joint beamforming power control and interference coordination"):
  with members:
    - "a deep learning framework for optimization of miso downlink beamforming"
    - "deep learning for tdd and fdd massive mimo mapping channels in space and frequency"
    - "a framework for automated cellular network tuning with reinforcement learning"
    - "deep learning in downlink coordinated multipoint in new radio heterogeneous networks"
    - "q learning algorithm for volte closed loop power control in indoor small cells"
    - "partially blind handovers for mmwave new radio aided by sub 6 ghz lte signaling"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"

Connected through hyperedge HE-3 (induced by source paper "learning beam codebooks with neural networks towards environment aware mmwave mimo"):
  with members:
    - "deep learning for massive mimo with 1 bit adcs when more antennas need fewer pilots"
    - "deep learning for mmwave beam and blockage prediction using sub 6ghz channels"
    - "beam codebook design for 5g mmwave terminals"
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "deep learning for direct hybrid precoding in millimeter wave massive mimo systems"
    - "machine learning for reliable mmwave systems blockage prediction and proactive handoff"
    - "deep complex networks"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"

Connected through hyperedge HE-4 (induced by source paper "advanced receiver architectures for millimeter wave communications with low resolution adcs"):
  with members:
    - "two stage analog combining in hybrid beamforming systems with low resolution adcs"
    - "on low resolution adcs in practical 5g millimeter wave massive mimo systems"
    - "robust learning based ml detection for massive mimo systems with one bit quantized signals"
    - "a comparison of hybrid beamforming and digital beamforming with low resolution adcs for multiple users and imperfect csi"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "resolution adaptive hybrid mimo architectures for millimeter wave communications"
    - "hybrid architectures with few bit adc receivers achievable rates and energy rate tradeoffs"
    - "millimeter wave receiver efficiency a comprehensive comparison of beamforming schemes with low resolution adcs"

Connected through hyperedge HE-5 (induced by source paper "deep reinforcement learning based beam tracking for low latency services in vehicular networks"):
  with members:
    - "low complexity adaptive beam and channel tracking for mobile mmwave communications"
    - "exploiting wireless channel state information structures beyond linear correlations a deep learning approach"
    - "online learning for position aided millimeter wave beam training"
    - "time sequence channel inference for beam alignment in vehicular networks"

Connected through hyperedge HE-6 (induced by source paper "multiple antenna technologies for beyond 5g"):
  with members:
    - "intelligent reflecting surface versus decode and forward how large surfaces are needed to beat relaying"
    - "robust trajectory and resource allocation design for secure uav aided communications"
    - "a new look at cell free massive mimo making it practical with dynamic cooperation"
    - "scalability aspects of cell free massive mimo"
    - "cascaded channel estimation for large intelligent metasurface assisted massive mimo"
    - "robust and secure wireless communications via intelligent reflecting surfaces"
    - "channel estimation for intelligent reflecting surface assisted multiuser communications framework algorithms and analysis"
    - "resource allocation for secure irs assisted multiuser miso systems"

Connected through hyperedge HE-7 (induced by source paper "interplay between noma and other emerging technologies a survey"):
  with members:
    - "secure relaying in non orthogonal multiple access trusted and untrusted scenarios"
    - "securing downlink massive mimo noma networks with artificial noise"
    - "lens based millimeter wave reconfigurable antenna noma"
    - "what role can noma play in massive mimo"
    - "noma an information theoretic perspective"
    - "joint power and time allocation for noma mec offloading"
    - "multi beam noma for hybrid mmwave systems"
    - "exploiting full half duplex user relaying in noma systems"

Connected through hyperedge HE-8 (induced by source paper "smart radio environments empowered by ai reconfigurable meta surfaces an idea whose time has come"):
  with members:
    - "a novel communication paradigm for high capacity and security via programmable indoor wireless environments in next generation wireless systems"
    - "reflection probability in wireless networks with metasurface coated environmental objects an approach based on random spatial processes"
    - "a tractable analysis of the blind spot probability in localization networks under correlated blocking"
    - "using any surface to realize a new paradigm for wireless communications"
    - "wireless network intelligence at the edge"
    - "medium access control in wireless network on chip a context analysis"
    - "beyond massive mimo the potential of positioning with large intelligent surfaces"
    - "realizing wireless communication through software defined hypersurface environments"

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)