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: "overlapping multihop clustering for wireless sensor networks"
  Abstract: Clustering is a standard approach for achieving efficient and scalable performance in wireless sensor networks. Traditionally, clustering algorithms aim at generating a number of disjoint clusters that satisfy some criteria. In this paper, we formulate a novel clustering problem that aims at generating overlapping multihop clusters. Overlapping clusters are useful in many sensor network applications, including intercluster routing, node localization, and time synchronization protocols. We also propose a randomized, distributed multihop clustering algorithm (KOCA) for solving the overlapping clustering problem. KOCA aims at generating connected overlapping clusters that cover the entire sensor network with a specific average overlapping degree. Through analysis and simulation experiments, we show how to select the different values of the parameters to achieve the clustering process objectives. Moreover, the results show that KOCA produces approximately equal-sized clusters, which allow distributing the load evenly over different clusters. In addition, KOCA is scalable; the clustering formation terminates in a constant time regardless of the network size.

Connected through hyperedge HE-1 (induced by source paper "optimizing the number of fog nodes for cloud fog thing networks"):
  with members:
    - "a survey on mobile edge computing the communication perspective"
    - "fog computing based radio access networks issues and challenges"
    - "fog computing focusing on mobile users at the edge"
    - "analyzing the impact of access point density on the performance of finite area networks"
    - "distance distributions in finite uniformly random networks theory and applications"

Connected through hyperedge HE-2 (induced by source paper "hierarchical overlapping clustering of network data using cut metrics"):
  with members:
    - "functorial hierarchical clustering with overlaps"
    - "authorship attribution through function word adjacency networks"
    - "hierarchical quasi clustering methods for asymmetric networks"
    - "axiomatic construction of hierarchical clustering in asymmetric networks"
    - "detect overlapping and hierarchical community structure in networks"

Connected through hyperedge HE-3 (induced by source paper "networked computing in wireless sensor networks for structural health monitoring"):
  with members:
    - "model based event detection in wireless sensor networks"
    - "optimal strategies for computing symmetric boolean functions in collocated networks"

Connected through hyperedge HE-4 (induced by source paper "weighted centroid algorithm for estimating primary user location theoretical analysis and distributed implementation"):
  with members:
    - "range free localization with the radical line"

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)