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: "gaussian multiple access channels with one bit quantizer at the receiver"
  Abstract: The capacity region of a two-transmitter Gaussian multiple access channel (MAC) under average input power constraints is studied, when the receiver employs a zero-threshold one-bit analogue-to-digital converter (ADC). It is proven that the input distributions of the two transmitters that achieve the boundary points of the capacity region are discrete. Based on the position of a boundary point, upper bounds on the number of the mass points of the corresponding distributions are derived. Furthermore, a lower bound on the sum capacity is proposed that can be achieved by time division with power control. Finally, inspired by the numerical results, the proposed lower bound is conjectured to be tight.

Connected through hyperedge HE-1 (induced by source paper "on multiterminal communication over mimo channels with one bit adcs at the receivers"):
  with members:
    - "tradeoff between delay and high snr capacity in quantized mimo systems"
    - "on mimo channel capacity with output quantization constraints"
    - "low power analog to digital conversion in millimeter wave systems impact of resolution and bandwidth on performance"
    - "capacity analysis of one bit quantized mimo systems with transmitter channel state information"
    - "quantized massive mu mimo ofdm uplink"
    - "millimeter wave cellular wireless networks potentials and challenges"
    - "at low snr asymmetric quantizers are better"

Connected through hyperedge HE-2 (induced by source paper "low snr asymptotic rates of vector channels with one bit outputs"):
  with members:
    - "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"
    - "joint channel and data estimation for large mimo systems with low precision adcs"

Connected through hyperedge HE-3 (induced by source paper "on capacity achieving distributions over complex awgn channels under nonlinear power constraints and their applications to swipt"):
  with members:
    - "modulation design for wireless information and power transfer with nonlinear energy harvester modeling"
    - "on the capacity of swipt systems with a nonlinear energy harvesting circuit"
    - "wireless information and power transfer over an awgn channel nonlinearity and asymmetric gaussian signaling"
    - "prototyping and experimentation of a closed loop wireless power transmission with channel acquisition and waveform optimization"
    - "low complexity adaptive multisine waveform design for wireless power transfer"
    - "waveform design for wireless power transfer"
    - "waveform optimization for swipt with nonlinear energy harvester modeling"
    - "wireless information and power transfer nonlinearity waveform design and rate energy tradeoff"

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)