human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "fundamentals of large sensor networks connectivity capacity clocks and computation"):
  Abstract: Sensor networks potentially feature large numbers of nodes. The nodes can monitor and sense their environment over time, communicate with each other over a wireless network, and process information that they exchange with each other. They differ from data networks in that the network as a whole may be designed for a specific application. We study the theoretical foundations of such large-scale sensor networks. We address four fundamental organizational and operational issues related to large sensor networks: connectivity, capacity, clocks, and function computation. To begin with, a sensor network must be connected so that information can indeed be exchanged between nodes. The connectivity graph of an ad hoc network is modeled as a random graph and the critical range for asymptotic connectivity is determined, as well as the critical number of neighbors that a node needs to connect to. Next, given connectivity, we address the issue of how much data can be transported over the sensor network. We present fundamental bounds on capacity under several models, as well as architectural implications for how wireless communication should be organized. Temporal information is important both for the applications of sensor networks as well as their operation. We present fundamental bounds on the synchronizability of clocks in networks, and also present and analyze algorithms for clock synchronization. Finally, we turn to the issue of gathering relevant information, which sensor networks are designed to do. One needs to study optimal strategies for in-network aggregation of data, in order to reliably compute a composite function of sensor measurements, as well as the complexity of doing so. We address the issue of how such computation can be performed efficiently in a sensor network and the algorithms for doing so, for some classes of functions.

Includes members:
  Member "optimal strategies for computing symmetric boolean functions in collocated networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "networked computing in wireless sensor networks for structural health monitoring")
  Member "information theoretic bounds for multiround function computation in collocated networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed function computation over a rooted directed tree")
  Member "distributed source coding for interactive function computation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "round complexity of common randomness generation the amortized setting")
  Member "on information theoretic scaling laws for wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the performance of optimized dense device to device wireless networks")
  Member "lattices for distributed source coding jointly gaussian sources and reconstruction of a linear function":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "towards an algebraic network information theory simultaneous joint typicality decoding")
  Member "a deterministic approach to wireless relay networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the deterministic sum capacity of the multiple access wiretap channel")
  Member "hierarchical cooperation achieves optimal capacity scaling in ad hoc networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "wireless mesh networking with devices equipped with multi connectivity")

Question: Please tell me which class the source paper 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)