human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "extracting wyner s common information using polar codes and polar lattices"):
  Abstract: Explicit constructions of polar codes and polar lattices for both lossless and lossy Gray-Wyner problems are studied. Polar codes are employed to extract Wyner's common information of doubly symmetric binary source; polar lattices are then extended to extract that of a pair of Gaussian sources or multiple Gaussian sources. With regard to the discrete sources, the entire best-known region of the lossless Gray-Wyner problem are achieved by specifying the test channels to construct polar codes without time-sharing. As a result, we are able to give an interpretation that the Wyner's common information remains the same to the lossy case when the distortion is small [1]. Finally, the entire best-known lossy Gray-Wyner region for discrete sources can also be achieved using polar codes. With regard to the Gaussian sources, the best-known lossy Gray-Wyner region for bivariate Gaussian sources with a specific covariance matrix [1] can be achieved by using polar lattices. Moreover, we prove that extracting Wyner's common information of a pair of Gaussian sources is equivalent to implementing the lossy compression for a single Gaussian source, which implies that the common information can be extracted by a polar lattice for quantization. Furthermore, we extend this result to the case of multiple Gaussian sources.

Includes members:
  Member "distributed simulation of continuous random variables":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "constrained functional value under general convexity conditions with applications to distributed simulation")
  Member "gaussian secure source coding and wyner s common information":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "common information components analysis")
  Member "polar lattices for lossy compression":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "polar codes and polar lattices for the heegard berger problem")
  Member "construction of capacity achieving lattice codes polar lattices":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sparse regression codes")
  Member "the lossy common information of correlated sources":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "characterization of conditional independence and weak realizations of multivariate gaussian random variables applications to networks")
  Member "semantically secure lattice codes for the gaussian wiretap channel":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "semantically secure lattice codes for compound mimo channels")
  Member "achieving awgn channel capacity with lattice gaussian coding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "well rounded lattices towards optimal coset codes for gaussian and fading wiretap channels")
  Member "achieving the capacity of any dmc using only polar codes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "polar codes simplicity random codes durability")

Question: Please tell me which class the source paper 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)