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: "joint channel and data estimation for large mimo systems with low precision adcs"
  Abstract: The use of low precision (e.g., 1-3 bits) analog-to-digital convenors (ADCs) in very large multiple-input multiple-output (MIMO) systems is a technique to reduce cost and power consumption. In this context, nevertheless, it has been shown that the training duration is required to be {\em very large} just to obtain an acceptable channel state information (CSI) at the receiver. A possible solution to the quantized MIMO systems is joint channel-and-data (JCD) estimation. This paper first develops an analytical framework for studying the quantized MIMO system using JCD estimation. In particular, we use the Bayes-optimal inference for the JCD estimation and realize this estimator utilizing a recent technique based on approximate message passing. Large-system analysis based on the replica method is then adopted to derive the asymptotic performances of the JCD estimator. Results from simulations confirm our theoretical findings and reveal that the JCD estimator can provide a significant gain over conventional pilot-only schemes in the quantized MIMO system.

Connected through hyperedge HE-1 (induced by source paper "low snr asymptotic rates of vector channels with one bit outputs"):
  with members:
    - "gaussian multiple access channels with one bit quantizer at the receiver"
    - "throughput analysis of massive mimo uplink with low resolution adcs"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "uplink performance of wideband massive mimo with one bit adcs"
    - "how much training is needed in one bit massive mimo systems at low snr"
    - "mixed adc massive mimo"
    - "channel estimation and uplink achievable rates in one bit massive mimo systems"
    - "one bit massive mimo channel estimation and high order modulations"

Connected through hyperedge HE-2 (induced by source paper "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"):
  with members:
    - "mixed adc massive mimo"
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "mixed adc massive mimo detectors performance analysis and design optimization"
    - "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"
    - "limited feedback in multiple antenna systems with one bit quantization"
    - "capacity analysis of one bit quantized mimo systems with transmitter channel state information"
    - "performance limits of massive mimo systems based on bayes optimal inference"

Connected through hyperedge HE-3 (induced by source paper "an overview of signal processing techniques for millimeter wave mimo systems"):
  with members:
    - "limited feedback hybrid precoding for multi user millimeter wave systems"
    - "capacity analysis of one bit quantized mimo systems with transmitter channel state information"
    - "compressed sensing based multi user millimeter wave systems how many measurements are needed"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "tractable model for rate in self backhauled millimeter wave cellular networks"
    - "coverage and rate analysis for millimeter wave cellular networks"
    - "millimeter wave cellular wireless networks potentials and challenges"
    - "spatially sparse precoding in millimeter wave mimo systems"

Connected through hyperedge HE-4 (induced by source paper "efficient optimal joint channel estimation and data detection for massive mimo systems"):
  with members:
    - "optimality of large mimo detection via approximate message passing"
    - "massive mimo for next generation wireless systems"
    - "optimal non coherent data detection for massive simo wireless systems a polynomial complexity solution"
    - "blind pilot decontamination"
    - "massive mimo and small cells improving energy efficiency by optimal soft cell coordination"
    - "massive mu mimo downlink tdd systems with linear precoding and downlink pilots"
    - "massive mimo how many antennas do we need"
    - "glrt optimal noncoherent lattice decoding"

Connected through hyperedge HE-5 (induced by source paper "one bit massive mimo channel estimation and high order modulations"):
  with members:
    - "capacity analysis of one bit quantized mimo systems with transmitter channel state information"
    - "finite snr bounds on the sum rate capacity of rayleigh block fading multiple access channels with no a priori csi"
    - "massive mimo with 1 bit adc"
    - "at low snr asymmetric quantizers are better"
    - "on the gain of joint processing of pilot and data symbols in stationary rayleigh fading channels"
    - "mutual information of iid complex gaussian signals on block rayleigh faded channels"
    - "increased capacity per unit cost by oversampling"
    - "what is the value of joint processing of pilots and data in block fading channels"

Connected through hyperedge HE-6 (induced by source paper "capacity analysis of one bit quantized mimo systems with transmitter channel state information"):
  with members:
    - "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"
    - "millimeter wave cellular wireless networks potentials and challenges"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"
    - "multiple parameter estimation with quantized channel output"
    - "belief propagation based mimo detection operating on quantized channel output"

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)