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: "channel estimation techniques for quantized distributed reception in mimo systems"
  Abstract: The Internet of Things (IoT) could enable the development of cloud multiple-input multiple-output (MIMO) systems where internet-enabled devices can work as distributed transmission/reception entities. We expect that spatial multiplexing with distributed reception using cloud MIMO would be a key factor of future wireless communication systems. In this paper, we first review practical receivers for distributed reception of spatially multiplexed transmit data where the fusion center relies on quantized received signals conveyed from geographically separated receive nodes. Using the structures of these receivers, we propose practical channel estimation techniques for the block-fading scenario. The proposed channel estimation techniques rely on very simple operations at the received nodes while achieving near-optimal channel estimation performance as the training length becomes large.

Connected through hyperedge HE-1 (induced by source paper "channel estimation for spatially temporally correlated massive mimo systems with one bit adcs"):
  with members:
    - "channel estimation for one bit massive mimo systems exploiting spatio temporal correlations"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "hybrid architectures with few bit adc receivers achievable rates and energy rate tradeoffs"
    - "mixed adc massive mimo detectors performance analysis and design optimization"
    - "channel estimation in broadband millimeter wave mimo systems with few bit adcs"
    - "near maximum likelihood detector and channel estimator for uplink multiuser massive mimo systems with one bit adcs"
    - "massive mimo for next generation wireless systems"

Connected through hyperedge HE-2 (induced by source paper "near maximum likelihood detector and channel estimator for uplink multiuser massive mimo systems with one bit adcs"):
  with members:
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "mixed adc massive mimo"
    - "low power analog to digital conversion in millimeter wave systems impact of resolution and bandwidth on performance"
    - "one bit massive mimo channel estimation and high order modulations"
    - "capacity analysis of one bit quantized mimo systems with transmitter channel state information"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "impact of residual transmit rf impairments on training based mimo systems"
    - "quantized massive mu mimo ofdm uplink"

Connected through hyperedge HE-3 (induced by source paper "coded distributed diversity a novel distributed reception technique for wireless communication systems"):
  with members:
    - "distributed reception with spatial multiplexing mimo systems for the internet of things"

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)