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: "performance optimization in mobile edge computing via deep reinforcement learning"
  Abstract: To improve the quality of computation experience for mobile devices, mobile-edge computing (MEC) is emerging as a promising paradigm by providing computing capabilities within radio access networks in close proximity. Nevertheless, the design of computation offloading policies for a MEC system remains challenging. Specifically, whether to execute an arriving computation task at local mobile device or to offload a task for cloud execution should adapt to the environmental dynamics in a smarter manner. In this paper, we consider MEC for a representative mobile user in an ultra dense network, where one of multiple base stations (BSs) can be selected for computation offloading. The problem of solving an optimal computation offloading policy is modelled as a Markov decision process, where our objective is to minimize the long-term cost and an offloading decision is made based on the channel qualities between the mobile user and the BSs, the energy queue state as well as the task queue state. To break the curse of high dimensionality in state space, we propose a deep $Q$-network-based strategic computation offloading algorithm to learn the optimal policy without having a priori knowledge of the dynamic statistics. Numerical experiments provided in this paper show that our proposed algorithm achieves a significant improvement in average cost compared with baseline policies.

Connected through hyperedge HE-1 (induced by source paper "wireless edge computing with latency and reliability guarantees"):
  with members:
    - "distributed federated learning for ultra reliable low latency vehicular communications"
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "how reliable and capable is multi connectivity"
    - "a tutorial on beam management for 3gpp nr at mmwave frequencies"
    - "uav communications for 5g and beyond recent advances and future trends"
    - "coded federated computing in wireless networks with straggling devices and imperfect csi"
    - "ultra reliable communication in 5g mmwave networks a risk sensitive approach"
    - "on device federated learning via blockchain and its latency analysis"

Connected through hyperedge HE-2 (induced by source paper "edge intelligence the confluence of edge computing and artificial intelligence"):
  with members:
    - "convergence of edge computing and deep learning a comprehensive survey"
    - "efficient hybrid network architectures for extremely quantized neural networks enabling intelligence at the edge"
    - "a graph neural network approach for scalable wireless power control"
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "scan a scalable neural networks framework towards compact and efficient models"
    - "vehicular edge computing via deep reinforcement learning"
    - "openei an open framework for edge intelligence"
    - "uav communications for 5g and beyond recent advances and future trends"

Connected through hyperedge HE-3 (induced by source paper "age of information aware radio resource management in vehicular networks a proactive deep reinforcement learning perspective"):
  with members:
    - "spectrum sharing in vehicular networks based on multi agent reinforcement learning"
    - "minimizing the age of information in wireless networks with stochastic arrivals"
    - "learning to entangle radio resources in vehicular communications an oblivious game theoretic perspective"
    - "optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning"
    - "multi tenant cross slice resource orchestration a deep reinforcement learning approach"
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "deep learning with long short term memory for time series prediction"
    - "deep reinforcement learning based resource allocation for v2v communications"

Connected through hyperedge HE-4 (induced by source paper "cross layer design for mission critical iot in mobile edge computing systems"):
  with members:
    - "improving network availability of ultra reliable and low latency communications with multi connectivity"
    - "asynchronous mobile edge computation offloading energy efficient resource management"
    - "energy efficient joint offloading and wireless resource allocation strategy in multi mec server systems"
    - "homa a receiver driven low latency transport protocol using network priorities"
    - "joint uplink and downlink resource configuration for ultra reliable and low latency communications"
    - "a survey on mobile edge computing the communication perspective"
    - "latency and reliability aware task offloading and resource allocation for mobile edge computing"
    - "delay optimal computation task scheduling for mobile edge computing systems"

Connected through hyperedge HE-5 (induced by source paper "multi tenant cross slice resource orchestration a deep reinforcement learning approach"):
  with members:
    - "low complexity distributed radio access network slicing algorithms and experimental results"
    - "distributed resource allocation for network slicing over licensed and unlicensed bands"
    - "dynamic network slicing for scalable fog computing systems with energy harvesting"
    - "network slicing for 5g with sdn nfv concepts architectures and challenges"
    - "a survey on mobile edge computing the communication perspective"
    - "near optimal behavior via approximate state abstraction"
    - "dynamic computation offloading for mobile edge computing with energy harvesting devices"
    - "from network sharing to multi tenancy the 5g network slice broker"

Connected through hyperedge HE-6 (induced by source paper "optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning"):
  with members:
    - "multipath transmission scheduling in millimeter wave cloud radio access networks"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "deep reinforcement learning for online computation offloading in wireless powered mobile edge computing networks"):
  with members:
    - "a survey on mobile edge computing the communication perspective"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "joint offloading and computing optimization in wireless powered mobile edge computing systems"
    - "energy efficient resource allocation for mobile edge computation offloading"
    - "wireless powered communication opportunities and challenges"
    - "deep reinforcement learning in large discrete action spaces"
    - "energy efficient mobile cloud computing powered by wireless energy transfer"
    - "efficient multi user computation offloading for mobile edge cloud computing"

Connected through hyperedge HE-8 (induced by source paper "deep reinforcement learning for online offloading in wireless powered mobile edge computing networks"):
  with members:
    - "a survey on mobile edge computing the communication perspective"
    - "learning based computation offloading for iot devices with energy harvesting"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "wireless powered communication opportunities and challenges"
    - "deep reinforcement learning in large discrete action spaces"
    - "continuous control with deep reinforcement 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)