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: "refined coding bounds and code constructions for coherent network error correction"
  Abstract: Coherent network error correction is the error-control problem in network coding with the knowledge of the network codes at the source and sink nodes. With respect to a given set of local encoding kernels defining a linear network code, we obtain refined versions of the Hamming bound, the Singleton bound, and the Gilbert-Varshamov bound for coherent network error correction. Similar to its classical counterpart, this refined Singleton bound is tight for linear network codes. The tightness of this refined bound is shown by two construction algorithms of linear network codes achieving this bound. These two algorithms illustrate different design methods: one makes use of existing network coding algorithms for error-free transmission and the other makes use of classical error-correcting codes. The implication of the tightness of the refined Singleton bound is that the sink nodes with higher maximum flow values can have higher error correction capabilities.

Connected through hyperedge HE-1 (induced by source paper "reliable and secure multishot network coding using linearized reed solomon codes"):
  with members:
    - "skew and linearized reed solomon codes and maximum sum rank distance codes over any division ring"
    - "relative generalized matrix weights of matrix codes for universal security on wire tap networks"
    - "sub quadratic decoding of gabidulin codes"
    - "relative generalized rank weight of linear codes and its applications to network coding"
    - "convolutional codes in rank metric with application to random network coding"
    - "convolutional codes with maximum column sum rank for network streaming"
    - "bounds on list decoding of rank metric codes"
    - "universal secure network coding via rank metric codes"

Connected through hyperedge HE-2 (induced by source paper "variable rate linear network error correction mds codes"):
  with members:
    - "universal secure network coding via rank metric codes"
    - "construction of network error correction codes in packet networks"
    - "on metrics for error correction in network coding"
    - "a rank metric approach to error control in random network coding"
    - "coding for errors and erasures in random network coding"

Connected through hyperedge HE-3 (induced by source paper "a matroidal framework for network error correcting codes"):
  with members:
    - "scalar linear solvability of matroidal networks associated with representable matroids"
    - "network error correction with unequal link capacities"
    - "construction algorithm for network error correcting codes attaining the singleton bound"

Connected through hyperedge HE-4 (induced by source paper "construction of network error correction codes in packet networks"):
  with members:
    - "a rank metric approach to error control in random network coding"
    - "coding for errors and erasures in random network coding"
    - "construction algorithm for network error correcting codes attaining the singleton bound"

Connected through hyperedge HE-5 (induced by source paper "network error correcting codes using small fields"):
  with members:
    - "construction of network error correction codes in packet networks"
    - "wireless network information flow"
    - "construction algorithm for network error correcting codes attaining the singleton bound"

Connected through hyperedge HE-6 (induced by source paper "protection against link errors and failures using network coding"):
  with members:
    - "overlay protection against link failures using network coding"
    - "a rank metric approach to error control 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)