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 "layered constructions for low delay streaming codes"):
  Abstract: We study error correction codes for multimedia streaming applications where a stream of source packets must be transmitted in real-time, with in-order decoding, and strict delay constraints. In our setup, the encoder observes a stream of source packets in a sequential fashion, and    $M$    channel packets must be transmitted between the arrival of successive source packets. Each channel packet can depend on all the source packets observed up to and including that time, but not on any future source packets. The decoder must reconstruct the source stream with a delay of    $T$    packets. We consider a class of packet erasure channels with burst and isolated erasures, where the erasure patterns are locally constrained. Our proposed model provides a tractable approximation to statistical models, such as the Gilbert–Elliott channel, for capacity analysis. When    $M=1$   , i.e., when the source-packet arrival and channel-packet transmission rates are equal, we establish upper and lower bounds on the capacity, that are within one unit of the decoding delay    $T$   . We also establish necessary and sufficient conditions on the column distance and column span of a convolutional code to be feasible, and in turn establish a fundamental tradeoff between these. Our proposed codes—maximum distance and span codes—achieve a near-optimal tradeoff between the column distance and column span, and involve a layered construction. When    $M>1$   , we establish the capacity for the burst-erasure channel and an achievable rate in the general case. Extensive numerical simulations over Gilbert–Elliott and Fritchman channel models suggest that our codes also achieve significant gains in the residual loss probability over statistical channel models.

Includes members:
  Member "streaming codes for multicast over burst erasure channels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an explicit construction of optimal streaming codes for channels with burst and arbitrary erasures")
  Member "robust streaming erasure codes based on deterministic channel approximations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust low delay streaming pir using convolutional codes")
  Member "erasure coding for real time streaming":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimal multiplexed erasure codes for streaming messages with different decoding delays")
  Member "round robin streaming with generations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimal finite length coding rate of random linear network coding schemes")
  Member "streaming codes for channels with burst and isolated erasures":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an explicit construction of optimal streaming codes for channels with burst and arbitrary erasures")
  Member "linear error correcting codes with anytime reliability":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "achievable moderate deviations asymptotics for streaming compression of correlated sources")
  Member "diversity embedded streaming erasure codes de sco constructions and optimality":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an explicit construction of optimal streaming codes for channels with burst and arbitrary erasures")
  Member "rateless codes for single server streaming to diverse users":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "streaming codes for multicast over burst erasure channels")

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)