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 "rate distortion for lossy in network function computation information dissipation and sequential reverse water filling"):
  Abstract: We consider the problem of distributed lossy linear function computation in a tree network. We examine two cases: (i) data aggregation (only one sink node computes) and (ii) consensus (all nodes compute the same function). By quantifying the accumulation of information loss in distributed computing, we obtain fundamental limits on network computation rate as a function of incremental distortions (and hence incremental loss of information) along the edges of the network. The above characterization, based on quantifying distortion accumulation, offers an improvement over classical cut-set type techniques which are based on overall distortions instead of incremental distortions. This quantification of information loss qualitatively resembles information dissipation in cascaded channels [1]. Surprisingly, this accumulation effect of distortion happens even at infinite blocklength. Combining this observation with an inequality on the dominance of mean-square quantities over relative-entropy quantities, we obtain outer bounds on the rate distortion function that are tighter than classical cut-set bounds by a difference which can be arbitrarily large in both data aggregation and consensus. We also obtain inner bounds on the optimal rate using random Gaussian coding, which differ from the outer bounds by $\mathcal{O}(\sqrt{D})$, where $D$ is the overall distortion. The obtained inner and outer bounds can provide insights on rate (bit) allocations for both the data aggregation problem and the consensus problem. We show that for tree networks, the rate allocation results have a mathematical structure similar to classical reverse water-filling for parallel Gaussian sources.

Includes members:
  Member "wasserstein continuity of entropy and outer bounds for interference channels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a lower bound on the expected distortion of joint source channel coding")
  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 "a generalized cut set bound for deterministic multi flow networks and its applications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "capacity region of the symmetric injective k user deterministic interference channel")
  Member "dissipation of information in channels with input constraints":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "broadcasting on random directed acyclic graphs")
  Member "a generalized typicality for abstract alphabets":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the renyi capacity and center")
  Member "strong data processing inequalities and phi sobolev inequalities for discrete channels":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on data processing and majorization inequalities for f divergences with applications")
  Member "on cooperation in multi terminal computation and rate distortion":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "intersensor collaboration in distributed quantization networks")
  Member "network coding for computing cut set bounds":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "expand and randomize an algebraic approach to secure computation")

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)