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: "unified offloading decision making and resource allocation in me ran"
  Abstract: In order to support communication and computation cooperation, we propose ME-RAN architecture, which consists of mobile edge cloud (ME) as the computation provision platform and radio access network (RAN) as the communication interface. Cooperative offloading framework is proposed to achieve the following tasks: (1) to increase user equipment' (UE') computing capacity by triggering offloading action, especially for the UE which cannot complete the computation locally; (2) to reduce the energy consumption for all the UEs by considering limited computing and communication resources. Based on above objectives, we formulate the energy consumption minimization problem, which is shown to be a non-convex mixed-integer programming. Firstly, Decentralized Local Decision Algorithm (DLDA) is proposed for each UE to estimate the possible local resource consumption and decide if offloading is in its interest. This operation will reduce the overhead and signalling in the later stage. Then, Centralized decision and resource Allocation algoRithm (CAR) is proposed to conduct the decision making and resource allocation in ME-RAN. Moreover, two low complexity algorithms, i.e., UE with largest saved energy consumption accepted first (CAR-E) and UE with smallest required data rate accepted first (CAR-D) are proposed. Simulations show that the performance of the proposed algorithms is very close to the exhaustive search but with much less complexity.

Connected through hyperedge HE-1 (induced by source paper "stacked auto encoder based deep reinforcement learning for online resource scheduling in large scale mec networks"):
  with members:
    - "deep reinforcement learning for online computation offloading in wireless powered mobile edge computing networks"
    - "deep reinforcement learning that matters"
    - "a survey on mobile edge computing the communication perspective"
    - "caching in the sky proactive deployment of cache enabled unmanned aerial vehicles for optimized quality of experience"
    - "prioritized experience replay"
    - "deep reinforcement learning with double q learning"
    - "adam a method for stochastic optimization"

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)