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: "efficient training management for mobile crowd machine learning a deep reinforcement learning approach"
  Abstract: In this letter, we consider the concept of Mobile Crowd-Machine Learning (MCML) for a federated learning model. The MCML enables mobile devices in a mobile network to collaboratively train neural network models required by a server while keeping data on the mobile devices. The MCML thus addresses data privacy issues of traditional machine learning. However, the mobile devices are constrained by energy, CPU, and wireless bandwidth. Thus, to minimize the energy consumption, training time and communication cost, the server needs to determine proper amounts of data and energy that the mobile devices use for training. However, under the dynamics and uncertainty of the mobile environment, it is challenging for the server to determine the optimal decisions on mobile device resource management. In this letter, we propose to adopt a deep- Q learning algorithm that allows the server to learn and find optimal decisions without any a priori knowledge of network dynamics. Simulation results show that the proposed algorithm outperforms the static algorithms in terms of energy consumption and training latency.

Connected through hyperedge HE-1 (induced by source paper "incentive design for efficient federated learning in mobile networks a contract theory approach"):
  with members:
    - "rewarding high quality data via influence functions"
    - "federated machine learning concept and applications"
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "federated learning for mobile keyboard prediction"
    - "multi objective evolutionary federated learning"
    - "blockchained on device federated learning"
    - "biscotti a ledger for private and secure peer to peer machine learning"
    - "incentive mechanism design for wireless energy harvesting based internet of things"

Connected through hyperedge HE-2 (induced by source paper "resource allocation in mobility aware federated learning networks a deep reinforcement learning approach"):
  with members:
    - "federated learning in mobile edge networks a comprehensive survey"
    - "deep reinforcement learning for time scheduling in rf powered backscatter cognitive radio networks"
    - "a survey on mobile edge computing the communication perspective"
    - "communication efficient learning of deep networks from decentralized data"
    - "tensorflow a system for large scale machine learning"
    - "deep reinforcement learning with double q learning"

Connected through hyperedge HE-3 (induced by source paper "reliable federated learning for mobile networks"):
  with members:
    - "toward intelligent vehicular networks a machine learning framework"
    - "blockchained on device federated learning"
    - "mitigating sybils in federated learning poisoning"
    - "federated learning with non iid data"
    - "biscotti a ledger for private and secure peer to peer machine learning"

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)