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: "mobile edge computing a survey on architecture and computation offloading"
  Abstract: Technological evolution of mobile user equipment (UEs), such as smartphones or laptops, goes hand-in-hand with evolution of new mobile applications. However, running computationally demanding applications at the UEs is constrained by limited battery capacity and energy consumption of the UEs. A suitable solution extending the battery life-time of the UEs is to offload the applications demanding huge processing to a conventional centralized cloud. Nevertheless, this option introduces significant execution delay consisting of delivery of the offloaded applications to the cloud and back plus time of the computation at the cloud. Such a delay is inconvenient and makes the offloading unsuitable for real-time applications. To cope with the delay problem, a new emerging concept, known as mobile edge computing (MEC), has been introduced. The MEC brings computation and storage resources to the edge of mobile network enabling it to run the highly demanding applications at the UE while meeting strict delay requirements. The MEC computing resources can be exploited also by operators and third parties for specific purposes. In this paper, we first describe major use cases and reference scenarios where the MEC is applicable. After that we survey existing concepts integrating MEC functionalities to the mobile networks and discuss current advancement in standardization of the MEC. The core of this survey is, then, focused on user-oriented use case in the MEC, i.e., computation offloading. In this regard, we divide the research on computation offloading to three key areas: 1) decision on computation offloading; 2) allocation of computing resource within the MEC; and 3) mobility management. Finally, we highlight lessons learned in area of the MEC and we discuss open research challenges yet to be addressed in order to fully enjoy potentials offered by the MEC.

Connected through hyperedge HE-1 (induced by source paper "fog computing systems state of the art research issues and future trends"):
  with members:
    - "5g network slicing using sdn and nfv a survey of taxonomy architectures and future challenges"
    - "a roadmap toward the resilient internet of things for cyber physical systems"
    - "game theory for multi access edge computing survey use cases and future trends"
    - "a blockchain enabled trustless crowd intelligence ecosystem on mobile edge computing"
    - "machine learning for resource management in cellular and iot networks potentials current solutions and open challenges"
    - "all one needs to know about fog computing and related edge computing paradigms a complete survey"
    - "mitigating jamming attacks using energy harvesting"
    - "game theoretical modelling of network cybersecurity"

Connected through hyperedge HE-2 (induced by source paper "energy efficient federated edge learning with joint communication and computation design"):
  with members:
    - "energy efficient radio resource allocation for federated edge learning"
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "energy efficient federated learning over wireless communication networks"
    - "joint communication and computation optimization for wireless powered mobile edge computing with d2d offloading"
    - "an overview of data importance aware radio resource management for edge machine learning"
    - "in edge ai intelligentizing mobile edge computing caching and communication by federated learning"
    - "robust and communication efficient federated learning from non iid data"
    - "edge ai on demand accelerating deep neural network inference via edge computing"

Connected through hyperedge HE-3 (induced by source paper "analysis on computation intensive status update in mobile edge computing"):
  with members:
    - "trading off computation with transmission in status update systems"
    - "age of information for computation intensive messages in mobile edge computing"
    - "timely cloud computing preemption and waiting"
    - "age of information in a network of preemptive servers"
    - "the age of information in networks moments distributions and sampling"
    - "joint status sampling and updating for minimizing age of information in the internet of things"
    - "joint information freshness and completion time optimization for vehicular networks"
    - "a survey on mobile edge computing the communication perspective"

Connected through hyperedge HE-4 (induced by source paper "distributed task replication for vehicular edge computing performance analysis and learning based algorithm"):
  with members:
    - "adaptive learning based task offloading for vehicular edge computing systems"
    - "exploiting moving intelligence delay optimized computation offloading in vehicular fog networks"
    - "heterogeneous coded computation across heterogeneous workers"
    - "hierarchical coded computation"
    - "speeding up distributed machine learning using codes"
    - "dense moving fog for intelligent iot key challenges and opportunities"
    - "learning based task offloading for vehicular cloud computing systems"
    - "a survey on mobile edge computing the communication perspective"

Connected through hyperedge HE-5 (induced by source paper "secel privacy preserving verifiable and fault tolerant edge learning for autonomous vehicles"):
  with members:
    - "advances and open problems in federated learning"
    - "efficient decentralized deep learning by dynamic model averaging"
    - "sdn based resource management for autonomous vehicular networks a multi access edge computing approach"
    - "federated learning for ultra reliable low latency v2v communications"
    - "towards an intelligent edge wireless communication meets machine learning"
    - "how to backdoor federated learning"
    - "a survey on mobile edge computing the communication perspective"
    - "deep models under the gan information leakage from collaborative deep learning"

Connected through hyperedge HE-6 (induced by source paper "communications caching computing tradeoff analysis for bidirectional data computation in mobile edge networks"):
  with members:
    - "bandwidth gain from mobile edge computing and caching in wireless multicast systems"
    - "communications caching and computing for mobile virtual reality modeling and tradeoff"
    - "a survey on mobile edge computing the communication perspective"
    - "joint pushing and caching for bandwidth utilization maximization in wireless networks"
    - "dynamic computation offloading for mobile edge computing with energy harvesting devices"
    - "the three primary colors of mobile systems"
    - "energy efficient resource allocation for mobile edge computation offloading"
    - "power delay tradeoff in multi user mobile edge computing systems"

Connected through hyperedge HE-7 (induced by source paper "device vs edge computing for mobile services delay aware decision making to minimize power consumption"):
  with members:
    - "joint task offloading scheduling and transmit power allocation for mobile edge computing systems"
    - "delay optimal computation task scheduling for mobile edge computing systems"
    - "energy efficient resource allocation for mobile edge computation offloading"
    - "joint uplink downlink optimization for backhaul limited mobile cloud computing with user scheduling"
    - "decentralized computation offloading game for mobile cloud computing"
    - "cloud offloading for multi radio enabled mobile devices"
    - "efficient multi user computation offloading for mobile edge cloud computing"

Connected through hyperedge HE-8 (induced by source paper "workload scheduling on heterogeneous mobile edge cloud in 5g networks to minimize sla violation"):
  with members:
    - "quantitative overfitting management for human in the loop ml application development with ease ml meter"
    - "emm energy aware mobility management for mobile edge computing in ultra dense networks"

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.IT(Information Theory)