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: "delay optimal power and subcarrier allocation for ofdma systems via stochastic approximation"
  Abstract: In this paper, we consider delay-optimal power and subcarrier allocation design for OFDMA systems with $N_F$ subcarriers, $K$ mobiles and one base station. There are $K$ queues at the base station for the downlink traffic to the $K$ mobiles with heterogeneous packet arrivals and delay requirements. We shall model the problem as a $K$-dimensional infinite horizon average reward Markov Decision Problem (MDP) where the control actions are assumed to be a function of the instantaneous Channel State Information (CSI) as well as the joint Queue State Information (QSI). This problem is challenging because it corresponds to a stochastic Network Utility Maximization (NUM) problem where general solution is still unknown. We propose an {\em online stochastic value iteration} solution using {\em stochastic approximation}. The proposed power control algorithm, which is a function of both the CSI and the QSI, takes the form of multi-level water-filling. We prove that under two mild conditions in Theorem 1 (One is the stepsize condition. The other is the condition on accessibility of the Markov Chain, which can be easily satisfied in most of the cases we are interested.), the proposed solution converges to the optimal solution almost surely (with probability 1) and the proposed framework offers a possible solution to the general stochastic NUM problem. By exploiting the birth-death structure of the queue dynamics, we obtain a reduced complexity decomposed solution with linear $\mathcal{O}(KN_F)$ complexity and $\mathcal{O}(K)$ memory requirement.

Connected through hyperedge HE-1 (induced by source paper "mimo amplify and forward precoding for networked control systems"):
  with members:
    - "optimal control strategies in delayed sharing information structures"
    - "pilot contamination and precoding in multi cell tdd systems"
    - "on the capacity and design of limited feedback multiuser mimo uplinks"
    - "limited feedback based block diagonalization for the mimo broadcast channel"

Connected through hyperedge HE-2 (induced by source paper "deep learning based wireless resource allocation with application to vehicular networks"):
  with members:
    - "learning to branch accelerating resource allocation in wireless networks"
    - "spectrum sharing in vehicular networks based on multi agent reinforcement learning"
    - "learning to detect"
    - "learning optimal resource allocations in wireless systems"
    - "deep learning based end to end wireless communication systems with conditional gan as unknown channel"
    - "distributed power control for large energy harvesting networks a multi agent deep reinforcement learning approach"
    - "power allocation in multi user cellular networks deep reinforcement learning approaches"
    - "toward intelligent vehicular networks a machine learning framework"

Connected through hyperedge HE-3 (induced by source paper "resource allocation in cloud radio access networks with device to device communications"):
  with members:
    - "energy efficient joint resource allocation and power control for d2d communications"
    - "energy efficient resource allocation optimization for multimedia heterogeneous cloud radio access networks"
    - "fog computing based radio access networks issues and challenges"
    - "recent advances in cloud radio access networks system architectures key techniques and open issues"
    - "energy efficient joint congestion control and resource optimization in heterogeneous cloud radio access networks"
    - "queue aware energy efficient joint remote radio head activation and beamforming in cloud radio access networks"
    - "inter tier interference suppression in heterogeneous cloud radio access networks"
    - "fronthaul constrained cloud radio access networks insights and challenges"

Connected through hyperedge HE-4 (induced by source paper "joint rate control and power allocation for non orthogonal multiple access systems"):
  with members:
    - "power domain non orthogonal multiple access noma in 5g systems potentials and challenges"
    - "power efficient resource allocation for mc noma with statistical channel state information"
    - "sub channel assignment power allocation and user scheduling for non orthogonal multiple access networks"
    - "a survey of downlink non orthogonal multiple access for 5g wireless communication networks"
    - "optimal joint power and subcarrier allocation for full duplex multicarrier non orthogonal multiple access systems"
    - "power and channel allocation for non orthogonal multiple access in 5g systems tractability and computation"
    - "the application of mimo to non orthogonal multiple access"
    - "power aware wireless file downloading a lyapunov indexing approach to a constrained restless bandit problem"

Connected through hyperedge HE-5 (induced by source paper "ultra reliable and low latency communications for connected vehicles challenges and solutions"):
  with members:
    - "reliable and efficient autonomous driving the need for heterogeneous vehicular networks"

Connected through hyperedge HE-6 (induced by source paper "energy efficient joint congestion control and resource optimization in heterogeneous cloud radio access networks"):
  with members:
    - "energy efficient resource allocation optimization for multimedia heterogeneous cloud radio access networks"
    - "queue aware energy efficient joint remote radio head activation and beamforming in cloud radio access networks"
    - "inter tier interference suppression in heterogeneous cloud radio access networks"
    - "contract based interference coordination in heterogeneous cloud radio access networks"
    - "energy efficient resource assignment and power allocation in heterogeneous cloud radio access networks"
    - "fronthaul constrained cloud radio access networks insights and challenges"
    - "system architecture and key technologies for 5g heterogeneous cloud radio access networks"
    - "heterogeneous cloud radio access networks a new perspective for enhancing spectral and energy efficiencies"

Connected through hyperedge HE-7 (induced by source paper "energy efficient resource allocation optimization for multimedia heterogeneous cloud radio access networks"):
  with members:
    - "inter tier interference suppression in heterogeneous cloud radio access networks"
    - "fronthaul constrained cloud radio access networks insights and challenges"
    - "system architecture and key technologies for 5g heterogeneous cloud radio access networks"
    - "joint power and antenna selection optimization in large cloud radio access networks"
    - "heterogeneous cloud radio access networks a new perspective for enhancing spectral and energy efficiencies"
    - "cache enabled opportunistic cooperative mimo for video streaming in wireless systems"
    - "energy efficiency and spectral efficiency tradeoff in device to device d2d communications"
    - "sparse beamforming and user centric clustering for downlink cloud radio access network"

Connected through hyperedge HE-8 (induced by source paper "resource allocation for cost minimization in limited feedback mu mimo systems with delay guarantee"):
  with members:
    - "interference aware resource control in multi antenna cognitive ad hoc networks with heterogeneous delay constraints"
    - "a survey on delay aware resource control for wireless systems large deviation theory stochastic lyapunov drift and distributed stochastic learning"
    - "queue aware dynamic clustering and power allocation for network mimo systems via distributed stochastic learning"
    - "multi mode transmission for the mimo broadcast channel with imperfect channel state information"
    - "mimo broadcast channels with finite rate feedback"

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.LG(Machine Learning)