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: "efficient dsp and circuit architectures for massive mimo state of the art and future directions"
  Abstract: Massive MIMO is a compelling wireless access concept that relies on the use of an excess number of base-station antennas, relative to the number of active terminals. This technology is a main component of 5G New Radio and addresses all important requirements of future wireless standards: a great capacity increase, the support of many simultaneous users, and improvement in energy efficiency. Massive MIMO requires the simultaneous processing of signals from many antenna chains, and computational operations on large matrices. The complexity of the digital processing has been viewed as a fundamental obstacle to the feasibility of Massive MIMO in the past. Recent advances on system-algorithm-hardware co-design have led to extremely energy-efficient implementations. These exploit opportunities in deeply-scaled silicon technologies and perform partly distributed processing to cope with the bottlenecks encountered in the interconnection of many signals. For example, prototype ASIC implementations have demonstrated zero-forcing precoding in real time at a 55 mW power consumption (20 MHz bandwidth, 128 antennas, and multiplexing of 8 terminals). Coarse and even error-prone digital processing in the antenna paths permits a reduction of consumption with a factor of 2 to 5. This article summarizes the fundamental technical contributions to efficient digital signal processing for Massive MIMO. The opportunities and constraints on operating on low-complexity RF and analog hardware chains are clarified. It illustrates how terminals can benefit from improved energy efficiency. The status of technology and real-life prototypes discussed. Open challenges and directions for future research are suggested.

Connected through hyperedge HE-1 (induced by source paper "decentralized equalization with feedforward architectures for massive mu mimo"):
  with members:
    - "gaussian message passing for overloaded massive mimo noma"
    - "feedforward architectures for decentralized precoding in massive mu mimo systems"
    - "decentralized baseband processing for massive mu mimo systems"
    - "optimally tuned nonparametric linear equalization for massive mu mimo systems"
    - "on the achievable rates of decentralized equalization in massive mu mimo systems"
    - "on the performance of mismatched data detection in large mimo systems"
    - "performance trade offs in multi processor approximate message passing"
    - "the world s first real time testbed for massive mimo design implementation and validation"

Connected through hyperedge HE-2 (induced by source paper "jamming suppression in massive mimo systems"):
  with members:
    - "multiple antennas secure transmission under pilot spoofing and jamming attack"
    - "jamming detection in massive mimo systems"
    - "securing massive mimo at the physical layer"
    - "energy and spectral efficiency of very large multiuser mimo systems"

Connected through hyperedge HE-3 (induced by source paper "massive mimo for drone communications case studies and future directions"):
  with members:
    - "convolutionally coded snr adaptive transmission for low latency communications"
    - "understanding uav cellular communications from existing networks to massive mimo"
    - "mobile networks connected drones field trials simulations and design insights"
    - "the sky is not the limit lte for unmanned aerial vehicles"
    - "a telecom perspective on the internet of drones from lte advanced to 5g"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "security privacy and safety evaluation of dynamic and static fleets of drones"
    - "massive mimo for communications with drone swarms"

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)