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: "coding for network coding"
  Abstract: We consider communication over a noisy network under randomized linear network coding. Possible error mechanism include node- or link- failures, Byzantine behavior of nodes, or an over-estimate of the network min-cut. Building on the work of Kotter and Kschischang, we introduce a probabilistic model for errors. We compute the capacity of this channel and we define an error-correction scheme based on random sparse graphs and a low-complexity decoding algorithm. By optimizing over the code degree profile, we show that this construction achieves the channel capacity in complexity which is jointly quadratic in the number of coded information bits and sublogarithmic in the error probability.

Connected through hyperedge HE-1 (induced by source paper "non binary ldpc codes with large alphabet size"):
  with members:
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "quantum error correction beyond the bounded distance decoding limit"
    - "spatially coupled binary mackay neal codes for channels with non binary inputs and affine subspace outputs"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "the effect of spatial coupling on compressive sensing"

Connected through hyperedge HE-2 (induced by source paper "rank minimization over finite fields fundamental limits and coding theoretic interpretations"):
  with members:
    - "information theoretic bounds for tensor rank minimization over finite fields"
    - "capacity achieving linear codes with random binary sparse generating matrices"
    - "capacity achieving random sparse linear codes"
    - "information theoretic bounds for low rank matrix completion"
    - "distributed storage codes meet multiple access wiretap channels"
    - "communication over finite field matrix channels"
    - "the power of convex relaxation near optimal matrix completion"
    - "lp decoding meets lp decoding a connection between channel coding and compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "spatially coupled binary mackay neal codes for channels with non binary inputs and affine subspace outputs"):
  with members:
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "threshold saturation on channels with memory via spatial coupling"
    - "spatially coupled mackay neal codes and hsu anastasopoulos codes"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "spatially coupled codes over the multiple access channel"
    - "universal rateless codes from coupled lt codes"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"

Connected through hyperedge HE-4 (induced by source paper "coding for linear operator channels over finite fields"):
  with members:
    - "on the capacity of non coherent network coding"
    - "communication over finite field matrix channels"
    - "combinatiorial algorithms for wireless information flow"
    - "multishot codes for network coding bounds and a multilevel construction"
    - "a rank metric approach to error control in random network coding"
    - "network coding for distributed storage systems"
    - "coding for errors and erasures in random network coding"
    - "wireless network information flow"

Connected through hyperedge HE-5 (induced by source paper "the capacity of random linear coding networks as subspace channels"):
  with members:
    - "on the capacity of non coherent network coding"
    - "multishot codes for network coding using rank metric codes"
    - "fast encoding and decoding of gabidulin codes"
    - "multishot codes for network coding bounds and a multilevel construction"
    - "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 "communication over finite field matrix channels"):
  with members:
    - "an algebraic approach to physical layer network coding"
    - "on metrics for error correction in network coding"
    - "compute and forward harnessing interference through structured codes"
    - "a rank metric approach to error control in random network coding"
    - "coding for errors and erasures in random network coding"

Connected through hyperedge HE-7 (induced by source paper "on the capacity of non coherent network coding"):
  with members:
    - "communication over finite field matrix channels"
    - "packing and covering properties of subspace codes for error control in random linear 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-8 (induced by source paper "multishot codes for network coding bounds and a multilevel construction"):
  with members:
    - "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"
    - "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)