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: "optimized power control for massive mimo with underlaid d2d communications"
  Abstract: In this paper, we consider device-to-device (D2D) communication that is underlaid in a multi-cell massive multiple-input multiple-output (MIMO) system and proposes a new framework for power control and pilot allocation. In this scheme, the cellular users (CUs) in each cell get orthogonal pilots which are reused with reuse factor one across cells, while all the D2D pairs share another set of orthogonal pilots. We derive a closed-form capacity lower bound for the CUs with different receive processing schemes. In addition, we derive a capacity lower bound for the D2D receivers and a closed-form approximation of it. We provide power control algorithms to maximize the minimum spectral efficiency (SE) and to maximize the product of the signal-to-interference-plus-noise ratios in the network. Different from prior works, in our proposed power control schemes, we consider joint pilot and data transmission optimization. Finally, we provide a numerical evaluation, where we compare our proposed power control schemes with the maximum transmit power case and the case of conventional multi-cell massive MIMO without D2D communication. Based on the provided results, we conclude that our proposed scheme increases the sum SE of multi-cell massive MIMO networks.

Connected through hyperedge HE-1 (induced by source paper "joint frame design and resource allocation for ultra reliable and low latency vehicular networks"):
  with members:
    - "ultra reliable and low latency communications for connected vehicles challenges and solutions"
    - "superimposed pilots are superior for mitigating pilot contamination in massive mimo"
    - "a two stage allocation scheme for delay sensitive services in dense vehicular networks"
    - "high reliability and low latency for vehicular networks challenges and solutions"
    - "generalized cross layer designs for generic half duplex multicarrier wireless networks with frequency reuse"
    - "reliable and efficient autonomous driving the need for heterogeneous vehicular networks"
    - "energy and spectral efficiency of very large multiuser mimo systems"
    - "information spectrum approach to second order coding rate in channel coding"

Connected through hyperedge HE-2 (induced by source paper "large scale fading precoding for maximizing the product of sinrs"):
  with members:
    - "joint user selection power allocation and precoding design with imperfect csit for multi cell mu mimo downlink systems"
    - "scalability aspects of cell free massive mimo"
    - "massive mimo with spatially correlated rician fading channels"
    - "large scale fading decoding in cellular massive mimo systems with spatially correlated channels"
    - "performance of cell free massive mimo with rician fading and phase shifts"
    - "uplink massive mimo for channels with spatial correlation"
    - "a bilinear equalizer for massive mimo systems"
    - "massive mimo has unlimited capacity"

Connected through hyperedge HE-3 (induced by source paper "power control in cellular massive mimo with varying user activity a deep learning solution"):
  with members:
    - "sum spectral efficiency maximization in massive mimo systems benefits from deep learning"
    - "joint transmit and circuit power minimization in massive mimo with downlink sinr constraints when to turn on massive mimo"
    - "large scale fading decoding in cellular massive mimo systems with spatially correlated channels"
    - "learning to optimize training deep neural networks for interference management"
    - "model aided wireless artificial intelligence embedding expert knowledge in deep neural networks towards wireless systems optimization"
    - "residual dense network for image super resolution"
    - "an introduction to deep learning for the physical layer"
    - "uplink interference reduction in large scale antenna systems"

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)