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: "learning based frequency and time domain inter cell interference coordination in hetnets"
  Abstract: In this paper, we focus on inter-cell interference coordination (ICIC) techniques in heterogeneous network (HetNet) deployments, whereby macro- and picocells    autonomously   optimize their downlink transmissions with loose coordination. We model this    strategic   coexistence as a multi-agent system, aiming at joint interference management and cell association. Using tools from Reinforcement Learning (RL), agents (i.e., macro- and picocells) sense their environment and self-adapt based on local information to maximize their network performance. Specifically, we explore both time- and frequency domain ICIC scenarios and propose a    two-level   RL formulation. Here, picocells learn their optimal cell range expansion (CRE) bias and transmit power allocation, as well as appropriate frequency bands for multi-flow transmissions, in which a user equipment (UE) can be simultaneously served by two or more base stations (BSs) from macro- and pico-layers. To substantiate our theoretical findings, Long-Term Evolution Advanced (LTE-A) based system-level simulations are carried out in which our proposed approaches are compared with a number of baseline approaches, such as resource partitioning (RP), static CRE, and single-flow Carrier Aggregation (CA). Our proposed solutions yield substantial gains up to 125% compared to static ICIC approaches in terms of average UE throughput in the time domain. In the frequency domain, our proposed solutions yield gains up to 240% in terms of cell-edge UE throughput.

Connected through hyperedge HE-1 (induced by source paper "power allocation in multi user cellular networks deep reinforcement learning approaches"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "a machine learning approach for power allocation in hetnets considering qos"
    - "fractional programming for communication systems part i power control and beamforming"
    - "power allocation in multi user cellular networks with deep q learning approach"
    - "an introduction to deep learning for the physical layer"
    - "policy gradient methods for reinforcement learning with function approximation and action dependent baselines"
    - "deep learning for wireless physical layer opportunities and challenges"
    - "deep reinforcement learning an overview"

Connected through hyperedge HE-2 (induced by source paper "deep reinforcement learning based mode selection and resource management for green fog radio access networks"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "on reducing iot service delay via fog offloading"
    - "layered group sparse beamforming for cache enabled green wireless networks"
    - "an evolutionary game for user access mode selection in fog radio access networks"
    - "deep reinforcement learning an overview"
    - "a deep reinforcement learning based framework for content caching"
    - "energy efficient resource allocation optimization for multimedia heterogeneous cloud radio access networks"
    - "fog computing based radio access networks issues and challenges"

Connected through hyperedge HE-3 (induced by source paper "analysis of handover failures in heterogeneous networks with fading"):
  with members:
    - "handover count based velocity estimation and mobility state detection in dense hetnets"
    - "what will 5g be"
    - "towards understanding the fundamentals of mobility in cellular networks"

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.NI(Networking and Internet Architecture)