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: "energy based modulation for noncoherent massive simo systems"
  Abstract: An uplink system with a single antenna transmitter and a single receiver with a large number of antennas is considered. We propose an energy-detection-based single-shot noncoherent communication scheme which does not use the instantaneous channel state information (CSI), but rather only the knowledge of the channel statistics. The suggested system uses a transmitter that modulates information on the power of the symbols, and a receiver which measures only the average energy across the antennas. We propose constellation designs which are asymptotically optimal with respect to symbol error rate (SER) with an increasing number of antennas, for any finite signal to noise ratio (SNR) at the receiver, under different assumptions on the availability of CSI statistics (exact channel fading distribution or the first few moments of the channel fading distribution). We also consider the case of imperfect knowledge of the channel statistics and describe in detail the case when there is a bounded uncertainty on the moments of the fading distribution. We present numerical results on the SER performance achieved by these designs in typical scenarios and find that they may outperform existing noncoherent constellations, e.g., conventional Amplitude Shift Keying (ASK), and pilot-based schemes, e.g., Pulse Amplitude Modulation (PAM). We also observe that an optimized constellation for a specific channel distribution makes it very sensitive to uncertainties in the channel statistics. In particular, constellation designs based on optimistic channel conditions could lead to significant performance degradation in terms of the achieved symbol error rates.

Connected through hyperedge HE-1 (induced by source paper "deep energy autoencoder for noncoherent multicarrier mu simo systems"):
  with members:
    - "channel agnostic end to end learning based communication systems with conditional gan"
    - "end to end learning of communications systems without a channel model"
    - "an introduction to deep learning for the physical layer"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "massive machine type communications in 5g physical and mac layer solutions"
    - "deep learning in neural networks"
    - "performance analysis of energy detection based massive simo"

Connected through hyperedge HE-2 (induced by source paper "joint constellation design for the two user non coherent multiple access channel"):
  with members:
    - "cube split a structured grassmannian constellation for non coherent simo communications"
    - "the optimal dof region for the two user non coherent simo multiple access channel"
    - "manopt a matlab toolbox for optimization on manifolds"

Connected through hyperedge HE-3 (induced by source paper "capacity scaling in a non coherent wideband massive simo block fading channel"):
  with members:
    - "massive mimo has unlimited capacity"
    - "unified capacity limit of non coherent wideband fading channels"
    - "how much spectrum is too much in millimeter wave wireless access"
    - "massive mimo ten myths and one critical question"
    - "massive mimo for maximal spectral efficiency how many users and pilots should be allocated"
    - "low snr capacity of noncoherent fading channels"
    - "capacity of sparse multipath channels in the ultra wideband regime"

Connected through hyperedge HE-4 (induced by source paper "noncoherent and non orthogonal massive simo for critical industrial iot communications"):
  with members:
    - "fractional programming for communication systems part i power control and beamforming"
    - "ultra reliable low latency cellular networks use cases challenges and approaches"
    - "quadrature amplitude modulation division for multiuser miso broadcast channels"
    - "design and performance analysis of noncoherent detection systems with massive receiver arrays"
    - "large system analysis of linear precoding in correlated miso broadcast channels under limited feedback"

Connected through hyperedge HE-5 (induced by source paper "on the number of rf chains and phase shifters and scheduling design with hybrid analog digital beamforming"):
  with members:
    - "mmwave massive mimo based wireless backhaul for the 5g ultra dense network"
    - "massive mimo performance evaluation based on measured propagation data"
    - "spatially sparse precoding in millimeter wave mimo systems"
    - "low complexity hybrid precoding in massive multiuser mimo systems"
    - "millimeter wave channel modeling and cellular capacity evaluation"
    - "millimeter wave beamforming for wireless backhaul and access in small cell networks"
    - "coding the beams improving beamforming training in mmwave communication system"
    - "energy and spectral efficiency of very large multiuser mimo systems"

Connected through hyperedge HE-6 (induced by source paper "design and performance analysis of noncoherent detection systems with massive receiver arrays"):
  with members:
    - "performance limits of energy detection systems with massive receiver arrays"
    - "performance analysis of energy detection based massive simo"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "effects of channel aging in massive mimo systems"

Connected through hyperedge HE-7 (induced by source paper "performance limits of energy detection systems with massive receiver arrays"):
  with members:
    - "scaling up mimo opportunities and challenges with very large arrays"

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)