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 "a simplified criterion for mdp convolutional codes"):
  Abstract: Maximum distance profile (MDP) convolutional codes have the property that their column distances are as large as possible. There exists a well-known criterion to check whether a code is MDP using the parity-check matrix of the code. However, this criterion has only been shown under the assumption that the parity-check matrix is left prime. Proving left primeness of a polynomial matrix is not an easy task and also, in almost all the previous papers which provide a construction of MDP convolutional codes, there is a gap in the application of the mentioned criterion to show the MDP property, since it is not proven that the parity-check matrix is left prime. In this paper we close this gap. In particular, we show that under the assumption that $(n-k)\mid\delta$ or $k\mid\delta$, a polynomial matrix that fulfills the MDP criterion is actually always left prime. Moreover, when $(n-k)\nmid\delta$ and $k\nmid\delta$, we show that the MDP criterion is not enough to ensure left primeness. In this case, with one more assumption, we can guarantee the result.

Includes members:
  Member "complete mdp convolutional codes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adaptive causal network coding with feedback")
  Member "a new class of superregular matrices and mdp convolutional codes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "locally repairable convolutional codes with sliding window repair")
  Member "decoding of convolutional codes over the erasure channel":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "robust low delay streaming pir using convolutional codes")

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)