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: "linear coding schemes for the distributed computation of subspaces"
  Abstract: Let X1, ..., Xm be a set of m statistically dependent sources over the common alphabet Fq, that are linearly independent when considered as functions over the sample space. We consider a distributed function computation setting in which the receiver is interested in the lossless computation of the elements of an s-dimensional subspace W spanned by the elements of the row vector [X1, ..., Xm]Γ in which the (m × s) matrix Γ has rank s. A sequence of three increasingly refined approaches is presented, all based on linear encoders. The first approach uses a common matrix to encode all the sources and a Korner-Marton like receiver to directly compute W. The second improves upon the first by showing that it is often more efficient to compute a carefully chosen superspace U of W. The superspace is identified by showing that the joint distribution of the {Xi} induces a unique decomposition of the set of all linear combinations of the {Xi}, into a chain of subspaces identified by a normalized measure of entropy. This subspace chain also suggests a third approach, one that employs nested codes. For any joint distribution of the {Xi} and any W, the sum-rate of the nested code approach is no larger than that under the Slepian-Wolf (SW) approach. Under the SW approach, W is computed by first recovering each of the {Xi}. For a large class of joint distributions and subspaces W, the nested code approach is shown to improve upon SW. Additionally, a class of source distributions and subspaces are identified, for which the nested-code approach is sum-rate optimal.

Connected through hyperedge HE-1 (induced by source paper "towards an algebraic network information theory simultaneous joint typicality decoding"):
  with members:
    - "homologous codes for multiple access channels"
    - "gaussian multiple access via compute and forward"
    - "polynomially solvable instances of the shortest and closest vector problems with applications to compute and forward"
    - "an achievable rate region for the three user interference channel based on coset codes"
    - "equivalence of additive combinatorial linear inequalities for shannon entropy and differential entropy"
    - "expanding the compute and forward framework unequal powers signal levels and multiple linear combinations"
    - "secure compute and forward in a bidirectional relay"
    - "distributed decode forward for relay networks"

Connected through hyperedge HE-2 (induced by source paper "condense a reconfigurable knowledge acquisition architecture for future 5g iot"):
  with members:
    - "network coding as a service"
    - "photonic delay systems as machine learning implementations"
    - "software defined networking a comprehensive survey"
    - "communication efficient distributed optimization using an approximate newton type method"
    - "distributed function computation over a rooted directed tree"
    - "internet of things iot a vision architectural elements and future directions"
    - "on network coding for sum networks"
    - "optoelectronic reservoir computing"

Connected through hyperedge HE-3 (induced by source paper "quasi linear codes application to point to point and multi terminal source coding"):
  with members:
    - "an achievable rate distortion region for multiple descriptions source coding based on coset codes"
    - "combinatorial message sharing and a new achievable region for multiple descriptions"
    - "achievable rate region for three user discrete broadcast channel based on coset 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)