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: "on the capacity of vector gaussian channels with bounded inputs"
  Abstract: The capacity of a deterministic multiple-input multiple-output (MIMO) channel under the peak and average power constraints is investigated. For the identity channel matrix, the approach of Shamai et al. is generalized to the higher dimension settings to derive the necessary and sufficient conditions for the optimal input probability density function. This approach prevents the usage of the identity theorem of the holomorphic functions of several complex variables which seems to fail in the multi-dimensional scenarios. It is proved that the support of the capacity-achieving distribution is a finite set of hyper-spheres with mutual independent phases and amplitude in the spherical domain. Subsequently, it is shown that when the average power constraint is relaxed, if the number of antennas is large enough, the capacity has a closed form solution and constant amplitude signaling at the peak power achieves it. Moreover, it will be observed that in a discrete-time memoryless Gaussian channel, the average power constrained capacity, which results from a Gaussian input distribution, can be closely obtained by an input where the support of its magnitude is a discrete finite set. Finally, we investigate some upper and lower bounds for the capacity of the non-identity channel matrix and evaluate their performance as a function of the condition number of the channel.

Connected through hyperedge HE-1 (induced by source paper "finite blocklength analysis of gaussian random coding in awgn channels under covert constraints"):
  with members:
    - "first and second order asymptotics in covert communication"
    - "fundamental limits of covert communication over mimo awgn channel"
    - "covert communication in fading channels under channel uncertainty"
    - "covert wireless communication with artificial noise generation"
    - "covert communication with finite blocklength in awgn channels"
    - "on covert communication with noise uncertainty"
    - "fundamental limits of communication with low probability of detection"
    - "covert communication over noisy channels a resolvability perspective"

Connected through hyperedge HE-2 (induced by source paper "the capacity achieving distribution for the amplitude constrained additive gaussian channel an upper bound on the number of mass points"):
  with members:
    - "upper and lower bounds on the capacity of amplitude constrained mimo channels"
    - "an upper bound for the capacity of amplitude constrained scalar awgn channel"
    - "capacity bounds for discrete time amplitude constrained additive white gaussian noise channels"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-3 (induced by source paper "on the capacity of the peak power constrained vector gaussian channel an estimation theoretic perspective"):
  with members:
    - "upper and lower bounds on the capacity of amplitude constrained mimo channels"
    - "capacity bounds for discrete time amplitude constrained additive white gaussian noise channels"
    - "peak to average power ratio of good codes for gaussian channel"
    - "pointwise relations between information and estimation in gaussian noise"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-4 (induced by source paper "on capacity achieving distributions over complex awgn channels under nonlinear power constraints and their applications to swipt"):
  with members:
    - "gaussian multiple access channels with one bit quantizer at the receiver"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "upper and lower bounds on the capacity of amplitude constrained mimo channels"):
  with members:
    - "the capacity of gaussian mimo channels under total and per antenna power constraints"
    - "an upper bound for the capacity of amplitude constrained scalar awgn channel"
    - "interference as noise friend or foe"
    - "capacity bounds for discrete time amplitude constrained additive white gaussian noise channels"
    - "miso capacity with per antenna power constraint"
    - "on the boundedness of the support of optimal input measures for rayleigh fading channels"

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)