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: "hierarchical fog cloud computing for iot systems a computation offloading game"
  Abstract: Fog computing, which provides low-latency computing services at the network edge, is an enabler for the emerging Internet of Things (IoT) systems. In this paper, we study the allocation of fog computing resources to the IoT users in a hierarchical computing paradigm including fog and remote cloud computing services. We formulate a computation offloading game to model the competition between IoT users and allocate the limited processing power of fog nodes efficiently. Each user aims to maximize its own quality of experience (QoE), which reflects its satisfaction of using computing services in terms of the reduction in computation energy and delay. Utilizing a potential game approach, we prove the existence of a pure Nash equilibrium and provide an upper bound for the price of anarchy. Since the time complexity to reach the equilibrium increases exponentially in the number of users, we further propose a near-optimal resource allocation mechanism and prove that in a system with $N$ IoT users, it can achieve an $\epsilon$-Nash equilibrium in $O(N/\epsilon)$ time. Through numerical studies, we evaluate the users' QoE as well as the equilibrium efficiency. Our results reveal that by utilizing the proposed mechanism, more users benefit from computing services in comparison to an existing offloading mechanism. We further show that our proposed mechanism significantly reduces the computation delay and enables low-latency fog computing services for delay-sensitive IoT applications.

Connected through hyperedge HE-1 (induced by source paper "a scalable iot fog framework for urban sound sensing"):
  with members:
    - "using cloud and fog computing for large scale iot based urban sound classification"
    - "fog computing survey of trends architectures requirements and research directions"
    - "deep learning for iot big data and streaming analytics a survey"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"

Connected through hyperedge HE-2 (induced by source paper "joint data compression and computation offloading in hierarchical fog cloud systems"):
  with members:
    - "latency optimization for resource allocation in mobile edge computation offloading"
    - "a survey on mobile edge computing the communication perspective"
    - "joint computation offloading and resource allocation in cloud based wireless hetnets"
    - "joint offloading and computing optimization in wireless powered mobile edge computing systems"
    - "multi antenna noma for computation offloading in multiuser mobile edge computing systems"
    - "a comprehensive survey on fog computing state of the art and research challenges"
    - "energy efficient resource allocation for mobile edge computation offloading"
    - "cell free massive mimo versus small cells"

Connected through hyperedge HE-3 (induced by source paper "process migration based computational offloading framework for iot supported mobile edge cloud computing"):
  with members:
    - "on reducing iot service delay via fog offloading"
    - "edge cloud offloading algorithms issues methods and perspectives"
    - "a survey on mobile edge computing the communication perspective"
    - "dmtcp transparent checkpointing for cluster computations and the desktop"

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.DC(Distributed, Parallel, and Cluster Computing)