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: "transmit power minimization in small cell networks under time average qos constraints"
  Abstract: We consider a small cell network (SCN) consisting of    $N$   cells, with the small cell base stations (SCBSs) equipped with    $N_t \geq 1$   antennas each, serving    $K$   single antenna user terminals (UTs) per cell. Under this set up, we address the following question: given certain time average quality of service (QoS) targets for the UTs, what is the minimum transmit power expenditure with which they can be met? Our motivation to consider time average QoS constraint comes from the fact that modern wireless applications such as file sharing, multi-media etc. allow some flexibility in terms of their delay tolerance. Time average QoS constraints can lead to greater transmit power savings as compared to instantaneous QoS constraints since it provides the flexibility to dynamically allocate resources over the fading channel states. We formulate the problem as a stochastic optimization problem whose solution is the design of the downlink beamforming vectors during each time slot. We solve this problem using the approach of Lyapunov optimization and characterize the performance of the proposed algorithm. With this algorithm as the reference, we present two main contributions that incorporate practical design considerations in SCNs. First, we analyze the impact of delays incurred in information exchange between the SCBSs. Second, we impose channel state information (CSI) feedback constraints, and formulate a joint CSI feedback and beamforming strategy. In both cases, we provide performance bounds of the algorithm in terms of satisfying the QoS constraints and the time average power expenditure. Our simulation results show that solving the problem with time average QoS constraints provide greater savings in the transmit power as compared to the instantaneous QoS constraints.

Connected through hyperedge HE-1 (induced by source paper "cross layer scheduling and beamforming in smart grid powered cellular networks with heterogeneous energy coordination"):
  with members:
    - "deep reinforcement learning for adaptive caching in hierarchical content delivery networks"
    - "optimal 3d trajectory design and resource allocation for solar powered uav communication systems"
    - "robust energy efficient beamforming in misome swipt systems with proportional secrecy rate"
    - "dynamic cross layer beamforming in hybrid powered communication systems with harvest use trade strategy"
    - "a hybrid energy sharing framework for green cellular networks"
    - "an overview of sustainable green 5g networks"
    - "ergodic energy management leveraging resource variability in distribution grids"
    - "comp meets smart grid a new communication and energy cooperation paradigm"

Connected through hyperedge HE-2 (induced by source paper "cross layer scheduling and beamforming in smart grid powered small cell networks"):
  with members:
    - "robust energy efficient beamforming in misome swipt systems with proportional secrecy rate"
    - "dynamic cross layer beamforming in hybrid powered communication systems with harvest use trade strategy"
    - "a hybrid energy sharing framework for green cellular networks"
    - "an overview of sustainable green 5g networks"
    - "power delay tradeoff with predictive scheduling in integrated cellular and wi fi networks"
    - "a general utility optimization framework for energy harvesting based wireless communications"

Connected through hyperedge HE-3 (induced by source paper "a satisfactory power control for 5g self organizing networks"):
  with members:
    - "ultra dense small cell networks turning density into energy efficiency"
    - "uav assisted heterogeneous networks for capacity enhancement"
    - "what will 5g be"
    - "quality of service provisioning in decentralized networks a satisfaction equilibrium approach"

Connected through hyperedge HE-4 (induced by source paper "dynamic resource allocation in next generation cellular networks with full duplex self backhauls"):
  with members:
    - "achievable rates of fdd massive mimo systems with spatial channel correlation"
    - "millimeter wave beamforming for wireless backhaul and access in small cell networks"
    - "scaling up mimo opportunities and challenges with very large arrays"

Connected through hyperedge HE-5 (induced by source paper "power delay tradeoff with predictive scheduling in integrated cellular and wi fi networks"):
  with members:
    - "energy efficient scheduling and power allocation in downlink ofdma networks with base station coordination"
    - "competition of wireless providers for atomic users"

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)