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: "a multi agent deep reinforcement learning based spectrum allocation framework for d2d underlay communications"
  Abstract: Device-to-device (D2D) underlay communication improves spectrum efficiency but causes severe interference, which requires an effective resource management solution without requiring global information. In this paper, a distributed spectrum allocation framework based on multi-agent deep reinforcement learning is proposed, named Neighbor-Agent Actor Critic (NAAC), to improve the D2D sum rate, while ensuring the quality of transmission of cellular users (CUEs). Each D2D pair is supported by an agent, which automatically selects a reasonable spectrum for transmission. For full cooperation between users, the proposed framework uses neighbor users' information for centralized training but is executed distributedly without that information, which not only ensures the convergence of the training but also improves the system performance. The simulation results show that the proposed method can effectively guarantee the transmission quality of the CUEs and improve the sum rate of D2D links, as well as have better convergence.

Connected through hyperedge HE-1 (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"
    - "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"
    - "tact a transfer actor critic learning framework for energy saving in cellular radio access networks"
    - "learning based constraint satisfaction with sensing restrictions"

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)