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 "erasure multiple descriptions"):
  Abstract: A binary erasure version of -channel multiple descriptions (MD) with symmetric descriptions (i.e., the rates of the descriptions are equal and the distortion constraint depends only on the number of messages received) is considered. No excess rate for every out of descriptions, i.e., any messages have sum rate , where is Shannon's rate-distortion function for erasure distortion and is the distortion constraint to be met, is investigated. The goal is to characterize the achievable distortions . Reconstruction fidelity is measured using two criteria: a worst-case criterion which computes distortion by maximizing the per-letter distortion over all source sequences, and an average-case criterion which computes distortion by averaging the per-letter distortion over all source sequences. Achievability schemes are presented, based on systematic maximum distance separable codes for worst-case distortion and random binning for average-case distortion, and optimality results are proved for the corresponding distortion regions. The erasure MD setup is then used to propose a layered coding framework for multiple descriptions, which is then applied to vector Gaussian MD and shown to be optimal for symmetric scalar Gaussian MD with two levels of receivers and no excess rate at the central receiver.

Includes members:
  Member "intermediate performance of rateless codes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "low density majority codes and the problem of graceful degradation")
  Member "vector gaussian multiple description with individual and central receivers":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "vector gaussian successive refinement with degraded side information")

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)