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: "decoding of convolutional codes over the erasure channel"
  Abstract: This paper studies the decoding capabilities of maximum distance profile (MDP) convolutional codes over the erasure channel and compares them with the decoding capabilities of MDS block codes over the same channel. The erasure channel involving large alphabets is an important practical channel model when studying packet transmissions over a network, e.g, the Internet.

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

Connected through hyperedge HE-2 (induced by source paper "robust low delay streaming pir using convolutional codes"):
  with members:
    - "private information retrieval from locally repairable databases with colluding servers"
    - "private information retrieval from coded storage systems with colluding byzantine and unresponsive servers"
    - "robust private information retrieval on coded data"
    - "layered constructions for low delay streaming codes"
    - "superregular matrices and applications to convolutional codes"
    - "private information retrieval from coded databases with colluding servers"
    - "the capacity of private information retrieval from coded databases"
    - "robust streaming erasure codes based on deterministic channel approximations"

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

Connected through hyperedge HE-4 (induced by source paper "necessary field size and probability for mdp and complete mdp convolutional codes"):
  with members:
    - "complete mdp convolutional codes"
    - "superregular matrices and applications to convolutional codes"
    - "a new class of superregular matrices and mdp convolutional codes"

Connected through hyperedge HE-5 (induced by source paper "ml and near ml decoding of ldpc codes over the bec bounds and decoding algorithms"):
  with members:
    - "distance properties of short ldpc codes and their impact on the bp ml and near ml decoding performance"
    - "searching for voltage graph based ldpc tailbiting codes with large girth"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "analysis of quasi cyclic ldpc codes under ml decoding over the erasure channel"
    - "maxwell construction the hidden bridge between iterative and maximum a posteriori decoding"

Connected through hyperedge HE-6 (induced by source paper "complete mdp convolutional codes"):
  with members:
    - "superregular matrices and applications to convolutional codes"
    - "a new class of superregular matrices and mdp convolutional codes"

Connected through hyperedge HE-7 (induced by source paper "convolutional codes with maximum column sum rank for network streaming"):
  with members:
    - "layered constructions for low delay streaming codes"
    - "on the impact of a single edge on the network coding capacity"
    - "convolutional codes in rank metric with application to random network coding"
    - "streaming codes for multicast over burst erasure channels"
    - "a new class of superregular matrices and mdp convolutional codes"
    - "multishot codes for network coding using rank metric codes"
    - "a rank metric approach to error control in random network coding"
    - "coding for errors and erasures in random network coding"

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)