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 "graph codes for distributed instant message collection in an arbitrary noisy broadcast network"):
  Abstract: We consider the problem of minimizing the number of broadcasts for collecting all sensor measurements at a sink node in a noisy broadcast sensor network. Focusing first on arbitrary network topologies, we provide (i) fundamental limits on the required number of broadcasts of data gathering, and (ii) a general in-network computing strategy to achieve an upper bound within factor $\log N$ of the fundamental limits, where $N$ is the number of agents in the network. Next, focusing on two example networks, namely, \textcolor{black}{arbitrary geometric networks and random Erd$\ddot{o}$s-R$\acute{e}$nyi networks}, we provide improved in-network computing schemes that are optimal in that they attain the fundamental limits, i.e., the lower and upper bounds are tight \textcolor{black}{in order sense}. Our main techniques are three distributed encoding techniques, called graph codes, which are designed respectively for the above-mentioned three scenarios. Our work thus extends and unifies previous works such as those of Gallager [1] and Karamchandani~\emph{et. al.} [2] on number of broadcasts for distributed function computation in special network topologies, while bringing in novel techniques, e.g., from error-control coding and noisy circuits, for both upper and lower bounds.

Includes members:
  Member "sum networks from undirected graphs construction and capacity analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sum networks from incidence structures construction and capacity analysis")
  Member "energy efficient distributed coding for data collection in a noisy sparse network":
    also connected through other hyperedges:
      (none)
  Member "computing linear transformations with unreliable components":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "numerically stable binary gradient coding")
  Member "update efficiency and local repairability limits for capacity approaching codes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "achieving multi port memory performance on single port memory with coding techniques")
  Member "computation over gaussian networks with orthogonal components":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "computing and communicating functions in disorganized wireless networks")
  Member "computing linear functions by linear coding over networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "how to distribute computation in networks")
  Member "network coding for computing cut set bounds":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "expand and randomize an algebraic approach to secure computation")
  Member "towards a queueing based framework for in network function computation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "random walk based in network computation of arbitrary functions")

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)