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: "spectral and energy efficiency of uplink d2d underlaid massive mimo cellular networks"
  Abstract: One of key 5G scenarios is that device-to-device (D2D) and massive multiple-input multiple-output (MIMO) will be co-existed. However, interference in the uplink D2D underlaid massive MIMO cellular networks needs to be coordinated, due to the vast cellular and D2D transmissions. To this end, this paper introduces a spatially dynamic power control solution for mitigating the cellular-to-D2D and D2D-to-cellular interference. In particular, the proposed D2D power control policy is rather flexible including the special cases of no D2D links or using maximum transmit power. Under the considered power control, an analytical approach is developed to evaluate the spectral efficiency (SE) and energy efficiency (EE) in such networks. Thus, the exact expressions of SE for a cellular user or D2D transmitter are derived, which quantify the impacts of key system parameters such as massive MIMO antennas and D2D density. Moreover, the D2D scale properties are obtained, which provide the sufficient conditions for achieving the anticipated SE. Numerical results corroborate our analysis and show that the proposed power control solution can efficiently mitigate interference between the cellular and D2D tier. The results demonstrate that there exists the optimal D2D density for maximizing the area SE of D2D tier. In addition, the achievable EE of a cellular user can be comparable to that of a D2D user.

Connected through hyperedge HE-1 (induced by source paper "simultaneous energy harvesting and information transmission in a mimo full duplex system a machine learning based design"):
  with members:
    - "generalized coordinated multipoint gcomp enabled noma outage capacity and power allocation"
    - "compact full duplex mimo radios in d2d underlaid cellular networks from system design to prototype results"
    - "a general design framework for mimo wireless energy transfer with limited feedback"
    - "continuous control with deep reinforcement learning"
    - "deep reinforcement learning with double q learning"
    - "a survey on device to device communication in cellular networks"
    - "in band full duplex wireless challenges and opportunities"
    - "wireless information and power transfer a dynamic power splitting approach"

Connected through hyperedge HE-2 (induced by source paper "optimized power control for massive mimo with underlaid d2d communications"):
  with members:
    - "power control for d2d underlay in multi cell massive mimo networks"
    - "joint pilot design and uplink power allocation in multi cell massive mimo systems"
    - "pilot reuse among d2d users in d2d underlaid massive mimo systems"
    - "energy efficiency and sum rate tradeoffs for massive mimo systems with underlaid device to device communications"
    - "the interplay between massive mimo and underlaid d2d networking"
    - "a survey on device to device communication in cellular networks"
    - "massive mimo for next generation wireless systems"
    - "what will 5g be"

Connected through hyperedge HE-3 (induced by source paper "edge and central cloud computing a perfect pairing for high energy efficiency and low latency"):
  with members:
    - "performance characterization of a real time massive mimo system with los mobile channels"
    - "a survey on mobile edge computing the communication perspective"
    - "mobile edge computing a survey on architecture and computation offloading"
    - "joint offloading and computing optimization in wireless powered mobile edge computing systems"
    - "user association in 5g networks a survey and an outlook"
    - "energy efficient resource allocation for mobile edge computing based augmented reality applications"
    - "energy efficient resource allocation for mobile edge computation offloading"
    - "computation offloading decisions for reducing completion time"

Connected through hyperedge HE-4 (induced by source paper "spatial motifs for device to device network analysis dna in cellular networks"):
  with members:
    - "fundamentals of cluster centric content placement in cache enabled device to device networks"
    - "modeling and performance analysis of clustered device to device networks"
    - "unmanned aerial vehicle with underlaid device to device communications performance and tradeoffs"
    - "context aware small cell networks how social metrics improve wireless resource allocation"
    - "a survey on device to device communication in cellular networks"
    - "cognitive and energy harvesting based d2d communication in cellular networks stochastic geometry modeling and analysis"
    - "modeling analysis and optimization of multicast device to device transmissions"
    - "resource management for device to device underlay communication"

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)