human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "complete mdp convolutional codes"
  Abstract: Maximum distance profile (MDP) convolutional codes have the property that their column distances are as large as possible. It has been shown that, transmitting over an erasure channel, these codes have optimal recovery rate for windows of a certain length. Reverse MDP convolutional codes have the additional advantage that they are suitable for forward and backward decoding algorithms. Beyond that the subclass of complete MDP convolutional codes has the ability to reduce the waiting time during decoding. The first main result of this paper is to show the existence and genericity of complete MDP convolutional codes for all code parameters. The second main contribution is the presentation of two concrete construction techniques to obtain complete MDP convolutional codes. These constructions work for all code parameters but require that the size of the underlying base field is (sufficiently) large.

Connected through hyperedge HE-1 (induced by source paper "a simplified criterion for mdp convolutional codes"):
  with members:
    - "a new class of superregular matrices and mdp convolutional codes"
    - "decoding of convolutional codes over the erasure channel"

Connected through hyperedge HE-2 (induced by source paper "adaptive causal network coding with feedback"):
  with members:
    - "resource allocation and harq optimization for urllc traffic in 5g wireless networks"
    - "tiny codes for guaranteeable delay"
    - "throughput maximization for delay sensitive random access communication"
    - "analysis of feedback error in automatic repeat request"
    - "low delay random linear coding over a stream"
    - "the effect of block wise feedback on the throughput delay trade off in streaming"
    - "an elias bound on the bhattacharyya distance of codes for channels with a zero error capacity"
    - "on the delay advantage of coding in packet erasure networks"

Connected through hyperedge HE-3 (induced by source paper "constructions of 2 1 2 complete j mdp convolutional codes"):
  with members:
    - "superregular matrices and applications to convolutional codes"
    - "a new class of superregular matrices and mdp convolutional codes"
    - "decoding of convolutional codes over the erasure channel"

Connected through hyperedge HE-4 (induced by source paper "constructions of mds convolutional codes using superregular matrices"):
  with members:
    - "superregular matrices and applications to convolutional codes"
    - "a new class of superregular matrices and mdp convolutional codes"
    - "a class of one dimensional mds convolutional codes"

Connected through hyperedge HE-5 (induced by source paper "tiny codes for guaranteeable delay"):
  with members:
    - "resource allocation and harq optimization for urllc traffic in 5g wireless networks"
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "the sky is not the limit lte for unmanned aerial vehicles"
    - "arq with cumulative feedback to compensate for burst errors"
    - "throughput maximization for delay sensitive random access communication"
    - "analysis of feedback error in automatic repeat request"
    - "low delay random linear coding over a stream"
    - "wide area wireless communication challenges for the internet of things"

Connected through hyperedge HE-6 (induced by source paper "necessary field size and probability for mdp and complete mdp convolutional codes"):
  with members:
    - "superregular matrices and applications to convolutional codes"
    - "a new class of superregular matrices and mdp convolutional codes"
    - "decoding of convolutional codes over the erasure channel"

Connected through hyperedge HE-7 (induced by source paper "analysis of coded selective repeat arq via matrix signal flow graphs"):
  with members:
    - "analysis of feedback error in automatic repeat request"
    - "low delay random linear coding over a stream"

Question: Please tell me which class the center node 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)