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 "robust analog function computation via wireless multiple access channels"):
  Abstract: Various wireless sensor network applications involve the computation of a pre-defined function of the measurements without the need for reconstructing each individual sensor reading. Widely-considered examples of such functions include the arithmetic mean and the maximum value. Standard approaches to the computation problem separate computation from communication: quantized sensor readings are transmitted interference-free to a fusion center that reconstructs each sensor reading and subsequently computes the sought function value. Such separation-based computation schemes are generally highly inefficient as a complete reconstruction of individual sensor readings is not necessary for the fusion center to compute a function of them. In particular, if the mathematical structure of the wireless channel is suitably matched (in some sense) to the function, then channel collisions induced by concurrent transmissions of different nodes can be beneficially exploited for computation purposes. Therefore, in this paper a practically relevant analog computation scheme is proposed that allows for an efficient estimate of linear and nonlinear functions over the wireless multiple-access channel. After analyzing the asymptotic properties of the estimation error, numerical simulations are presented to show the potential for huge performance gains when compared with time-division multiple-access based computation schemes.

Includes members:
  Member "reliable physical layer network coding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards massive connectivity support for scalable mmtc communications in 5g networks")
  Member "network coding for computing cut set bounds":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "expand and randomize an algebraic approach to secure computation")
  Member "distributed detection over gaussian multiple access channels with constant modulus signaling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "nomographic functions efficient computation in clustered gaussian sensor networks")
  Member "gossip algorithms for distributed signal processing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "inverse graph learning over optimization networks")

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.IT(Information Theory)