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: "projective space codes for the injection metric"
  Abstract: In the context of error control in random linear network coding, it is useful to construct codes that comprise well-separated collections of subspaces of a vector space over a finite field. In this paper, the metric used is the so-called "injection distance", introduced by Silva and Kschischang. A Gilbert-Varshamov bound for such codes is derived. Using the code-construction framework of Etzion and Silberstein, new non-constant-dimension codes are constructed; these codes contain more codewords than comparable codes designed for the subspace metric.

Connected through hyperedge HE-1 (induced by source paper "matrix codes as ideals for grassmannian codes and their weight properties"):
  with members:
    - "coding for errors and erasures in random network coding"

Connected through hyperedge HE-2 (induced by source paper "subspace codes based on graph matchings ferrers diagrams and pending blocks"):
  with members:
    - "partial spreads in random network coding"
    - "new improvements on the echelon ferrers construction"
    - "large constant dimension codes and lexicodes"
    - "recursive code construction for random networks"
    - "enumerative coding for grassmannian space"
    - "error correcting codes in projective spaces via rank metric codes and ferrers diagrams"
    - "spread codes and spread decoding in network coding"
    - "a rank metric approach to error control in random network coding"

Connected through hyperedge HE-3 (induced by source paper "optimal ferrers diagram rank metric codes"):
  with members:
    - "subspace codes based on graph matchings ferrers diagrams and pending blocks"
    - "subspace codes from ferrers diagrams"
    - "new lower bounds for constant dimension codes"
    - "new improvements on the echelon ferrers construction"
    - "recursive code construction for random networks"
    - "on metrics for error correction in network coding"
    - "error correcting codes in projective spaces via rank metric codes and ferrers diagrams"
    - "a rank metric approach to error control in random network coding"

Connected through hyperedge HE-4 (induced by source paper "bounds for projective codes from semidefinite programming"):
  with members:
    - "applications of semidefinite programming to coding theory"
    - "on metrics for error correction in network coding"
    - "error correcting codes in projective spaces via rank metric codes and ferrers diagrams"
    - "spread codes and spread decoding in network coding"
    - "construction of large constant dimension codes with a prescribed minimum distance"
    - "coding for errors and erasures in random network coding"
    - "johnson type bounds on constant dimension codes"

Connected through hyperedge HE-5 (induced by source paper "a singleton bound for lattice schemes"):
  with members:
    - "linearity and complements in projective space"
    - "codes and designs related to lifted mrd codes"
    - "network coding with modular lattices"
    - "error correcting codes in projective spaces via rank metric codes and ferrers diagrams"
    - "a rank metric approach to error control in random network coding"
    - "coding for errors and erasures in random network coding"

Connected through hyperedge HE-6 (induced by source paper "towards the capacity region of multiplicative linear operator broadcast channels"):
  with members:
    - "the capacity of random linear coding networks as subspace channels"
    - "on the capacity of non coherent network coding"
    - "recursive code construction for random networks"
    - "coding for linear operator channels over finite fields"
    - "communication over finite field matrix channels"
    - "multishot codes for network coding bounds and a multilevel construction"
    - "error correcting codes in projective spaces via rank metric codes and ferrers diagrams"
    - "a rank metric approach to error control in random network coding"

Connected through hyperedge HE-7 (induced by source paper "coding theory and projective spaces"):
  with members:
    - "recursive code construction for random networks"
    - "enumerative encoding in the grassmannian space"
    - "on metrics for error correction in network coding"
    - "packing and covering properties of subspace codes for error control in random linear network coding"
    - "construction of error correcting codes for random network coding"
    - "spread codes and spread decoding in network coding"
    - "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)