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: "capacity achieving random sparse linear codes"
  Abstract: In this paper the existence of capacity achieving linear codes with arbitrarily sparse generator matrices is proved. In particular, we show the existence of capacity achieving codes for which the density of ones in the generator matrix is arbitrarily low. The existing results on the existence of capacity achieving linear codes in the literature are limited to the codes whose generator matrix elements are zero or one with necessarily equal probability, yielding a non-sparse generator matrix. This will imply a high encoding complexity. An interesting trade-off between the sparsity of the generator matrix and the value of the error exponent is also demonstrated. Compared to the existing results in the literature, which are limited to codes with non-sparse generator matrices, the proposed approach is novel and more concise. Although the focus in this paper is on the Binary Symmetric and Binary Erasure Channels, the results can be easily extended to other discrete memory-less symmetric channels.

Connected through hyperedge HE-1 (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"
    - "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"
    - "guaranteed rank minimization via singular value projection"

Connected through hyperedge HE-2 (induced by source paper "error correcting codes for distributed control"):
  with members:
    - "potent tree codes and their applications coding for interactive communication revisited"

Connected through hyperedge HE-3 (induced by source paper "least squares superposition codes of moderate dictionary size reliable at rates up to capacity"):
  with members:
    - "toward fast reliable communication at rates near capacity with gaussian noise"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"
    - "necessary and sufficient conditions for sparsity pattern recovery"
    - "on off random access channels a compressed sensing framework"
    - "on the rate of channel polarization"
    - "shannon theoretic limits on noisy compressive sampling"
    - "asymptotically optimal multiple access communication via distributed rate splitting"
    - "channel coding the road to channel capacity"

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)