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: "binary error correcting network codes"
  Abstract: We consider network coding for networks experiencing worst-case bit-flip errors, and argue that this is a reasonable model for highly dynamic wireless network transmissions. We demonstrate that in this setup prior network error-correcting schemes can be arbitrarily far from achieving the optimal network throughput. We propose a new metric for errors under this model. Using this metric, we prove a new Hamming-type upper bound on the network capacity. We also show a commensurate lower bound based on GV-type codes that can be used for error-correction. The codes used to attain the lower bound are non-coherent (do not require prior knowledge of network topology). The end-to-end nature of our design enables our codes to be overlaid on classical distributed random linear network codes. Further, we free internal nodes from having to implement potentially computationally intensive link-by-link error-correction.

Connected through hyperedge HE-1 (induced by source paper "end to end error correcting codes on networks with worst case symbol errors"):
  with members:
    - "hybrid noncoherent network coding"
    - "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-2 (induced by source paper "near optimal quantization and linear network coding for relay networks"):
  with members:
    - "an algebraic approach to physical layer network coding"
    - "expansion coding achieving the capacity of an aen channel"
    - "multicommodity flows and cuts in polymatroidal networks"
    - "capacity of multiple unicast in wireless networks a polymatroidal approach"
    - "wireless network information flow a deterministic approach"
    - "low complexity near optimal codes for gaussian relay networks"
    - "a digital interface for gaussian relay and interference networks lifting codes from the discrete superposition model"
    - "approximately achieving gaussian relay network capacity with lattice codes"

Connected through hyperedge HE-3 (induced by source paper "hybrid noncoherent network coding"):
  with members:
    - "error resilience in distributed storage via rank metric codes"
    - "new improvements on the echelon ferrers construction"
    - "recursive code construction for random networks"
    - "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"
    - "construction of large constant dimension codes with a prescribed minimum distance"
    - "constant rank codes and their connection to constant dimension codes"

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)