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: "multipath transmission scheduling in millimeter wave cloud radio access networks"
  Abstract: Millimeter wave (mmWave) communications provide great potential for next-generation cellular networks to meet the demands of fast-growing mobile data traffic with plentiful spectrum available. However, in a mmWave cellular system, the shadowing and blockage effects lead to the intermittent connectivity, and the handovers are more frequent. This paper investigates an ``all-mmWave'' cloud radio access network (cloud-RAN), in which both the fronthaul and the radio access links operate at mmWave. To address the intermittent transmissions, we allow the mobile users (MUs) to establish multiple connections to the central unit over the remote radio heads (RRHs). Specifically, we propose a multipath transmission framework by leveraging the ``all-mmWave'' cloud-RAN architecture, which makes decisions of the RRH association and the packet transmission scheduling according to the time-varying network statistics, such that a MU experiences the minimum queueing delay and packet drops. The joint RRH association and transmission scheduling problem is formulated as a Markov decision process (MDP). Due to the problem size, a low-complexity online learning scheme is put forward, which requires no a priori statistic information of network dynamics. Simulations show that our proposed scheme outperforms the state-of-art baselines, in terms of average queue length and average packet dropping rate.

Connected through hyperedge HE-1 (induced by source paper "optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning"):
  with members:
    - "performance optimization in mobile edge computing via deep reinforcement learning"
    - "online learning for offloading and autoscaling in energy harvesting mobile edge computing"
    - "a survey on mobile edge computing the communication perspective"
    - "mobile edge computing a survey on architecture and computation offloading"
    - "latency and reliability aware task offloading and resource allocation for mobile edge computing"
    - "joint offloading and computing optimization in wireless powered mobile edge computing systems"
    - "delay optimal computation task scheduling for mobile edge computing systems"
    - "dynamic computation offloading for mobile edge computing with energy harvesting devices"

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)