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: "drag deep reinforcement learning based base station activation in heterogeneous networks"
  Abstract: Heterogeneous Network (HetNet), where Small cell Base Stations (SBSs) are densely deployed to offload traffic from macro Base Stations (BSs), is identified as a key solution to meet the unprecedented mobile traffic demand. The high density of SBSs are designed for peak traffic hours and consume an unnecessarily large amount of energy during off-peak time. In this paper, we propose a deep reinforcement-learning based SBS activation strategy that activates the optimal subset of SBSs to significantly lower the energy consumption without compromising the quality of service. In particular, we formulate the SBS on/off switching problem into a Markov Decision Process that can be solved by Actor Critic (AC) reinforcement learning methods. To avoid prohibitively high computational and storage costs of conventional tabular-based approaches, we propose to use deep neural networks to approximate the policy and value functions in the AC approach. Moreover, to expedite the training process, we adopt a Deep Deterministic Policy Gradient (DDPG) approach together with a novel action refinement scheme. Through extensive numerical simulations, we show that the proposed scheme greatly outperforms the existing methods in terms of both energy efficiency and computational efficiency. We also show that the proposed scheme can scale to large system with polynomial complexities in both storage and computation.

Connected through hyperedge HE-1 (induced by source paper "cellular traffic prediction and classification a comparative evaluation of lstm and arima"):
  with members:
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "self organized low power iot networks a distributed learning approach"
    - "stock price correlation coefficient prediction with arima lstm hybrid model"
    - "risk aware resource allocation for urllc challenges and strategies with machine learning"
    - "deep learning for encrypted traffic classification an overview"
    - "modeling long and short term temporal patterns with deep neural networks"
    - "artificial neural networks based machine learning for wireless networks a tutorial"
    - "neutm a neural network based framework for traffic matrix prediction in sdn"

Connected through hyperedge HE-2 (induced by source paper "user traffic prediction for proactive resource management learning powered approaches"):
  with members:
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "self organized low power iot networks a distributed learning approach"
    - "stock price correlation coefficient prediction with arima lstm hybrid model"
    - "risk aware resource allocation for urllc challenges and strategies with machine learning"
    - "modeling long and short term temporal patterns with deep neural networks"
    - "neutm a neural network based framework for traffic matrix prediction in sdn"

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)