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: "tact a transfer actor critic learning framework for energy saving in cellular radio access networks"
  Abstract: Recent works have validated the possibility of improving energy efficiency in radio access networks (RANs), achieved by dynamically turning on/off some base stations (BSs). In this paper, we extend the research over BS switching operations, which should match up with traffic load variations. Instead of depending on the dynamic traffic loads which are still quite challenging to precisely forecast, we firstly formulate the traffic variations as a Markov decision process. Afterwards, in order to foresightedly minimize the energy consumption of RANs, we design a reinforcement learning framework based BS switching operation scheme. Furthermore, to speed up the ongoing learning process, a transfer actor-critic algorithm (TACT), which utilizes the transferred learning expertise in historical periods or neighboring regions, is proposed and provably converges. In the end, we evaluate our proposed scheme by extensive simulations under various practical configurations and show that the proposed TACT algorithm contributes to a performance jumpstart and demonstrates the feasibility of significant energy efficiency improvement at the expense of tolerable delay performance.

Connected through hyperedge HE-1 (induced by source paper "intelligent resource slicing for embb and urllc coexistence in 5g and beyond a deep reinforcement learning based approach"):
  with members:
    - "extreme urllc vision challenges and key enablers"
    - "coexistence mechanism between embb and urllc in 5g wireless networks"
    - "embb urllc resource slicing a risk sensitive approach"
    - "ultra reliable and low latency vehicular transmission an extreme value theory approach"
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "coexistence of urllc and embb services in the c ran uplink an information theoretic study"
    - "5g wireless network slicing for embb urllc and mmtc a communication theoretic view"
    - "ultra reliable and low latency communications in 5g downlink physical layer aspects"

Connected through hyperedge HE-2 (induced by source paper "a comprehensive survey on transfer learning"):
  with members:
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "transfer learning toolkit primers and benchmarks"
    - "domain agnostic learning with disentangled representations"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "transfer learning with dynamic adversarial adaptation network"
    - "domain symmetric networks for adversarial domain adaptation"
    - "transferrable prototypical networks for unsupervised domain adaptation"
    - "deep visual domain adaptation a survey"

Connected through hyperedge HE-3 (induced by source paper "deep reinforcement learning with discrete normalized advantage functions for resource management in network slicing"):
  with members:
    - "elastic multi resource network slicing can protection lead to improved performance"
    - "continuous deep q learning with model based acceleration"
    - "prioritized experience replay"
    - "deep reinforcement learning in large discrete action spaces"
    - "continuous control with deep reinforcement learning"
    - "dueling network architectures for deep reinforcement learning"
    - "deep reinforcement learning with double q learning"

Connected through hyperedge HE-4 (induced by source paper "multi agent deep reinforcement learning based spectrum allocation for d2d underlay communications"):
  with members:
    - "spectrum sharing in vehicular networks based on multi agent reinforcement learning"
    - "a multi agent deep reinforcement learning based spectrum allocation framework for d2d underlay communications"
    - "deep reinforcement learning based resource allocation for v2v communications"
    - "deep reinforcement learning for resource allocation in v2v communications"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "continuous control with deep reinforcement learning"
    - "learning based constraint satisfaction with sensing restrictions"

Connected through hyperedge HE-5 (induced by source paper "application of machine learning in wireless networks key techniques and open issues"):
  with members:
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "learning to optimize training deep neural networks for interference management"
    - "deep reinforcement learning for resource management in network slicing"
    - "optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning"
    - "deep learning for radio resource allocation in multi cell networks"
    - "multi tenant cross slice resource orchestration a deep reinforcement learning approach"
    - "deep reinforcement learning based mode selection and resource management for green fog radio access networks"
    - "deep learning in mobile and wireless networking a survey"

Connected through hyperedge HE-6 (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-7 (induced by source paper "a survey on applications of model free strategy learning in cognitive wireless networks"):
  with members:
    - "machine learning in wireless sensor networks algorithms strategies and applications"
    - "power allocation with stackelberg game in femtocell networks a self learning approach"
    - "online reinforcement learning for dynamic multimedia systems"
    - "dynamic conjectures in random access networks using bio inspired learning"
    - "adaptive channel allocation spectrum etiquette for cognitive radio networks"
    - "accelerating reinforcement learning through implicit imitation"
    - "reinforcement learning a survey"

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)