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: "wyner ziv coding based on multidimensional nested lattices"
  Abstract: Distributed source coding (DSC) addresses the compression of correlated sources without communication links among them. This paper is concerned with the Wyner-Ziv problem: coding of an information source with side information available only at the decoder in the form of a noisy version of the source. Both the theoretical analysis and code design are addressed in the framework of multi-dimensional nested lattice coding (NLC). For theoretical analysis, accurate computation of the rate-distortion function is given under the high-resolution assumption, and a new upper bound using the derivative of the theta series is derived. For practical code design, several techniques with low complexity are proposed. Compared to the existing Slepian-Wolf coded nested quantization (SWC-NQ) for Wyner-Ziv coding based on one or two-dimensional lattices, our proposed multi-dimensional NLC can offer better performance at arguably lower complexity, since it does not require the second stage of Slepian-Wolf coding.

Connected through hyperedge HE-1 (induced by source paper "polar lattices for lossy compression"):
  with members:
    - "some goodness properties of lda lattices"
    - "lossy compression via sparse linear regression computationally efficient encoding and decoding"
    - "lossy compression via sparse linear regression performance under minimum distance encoding"
    - "construction of capacity achieving lattice codes polar lattices"
    - "on the construction of polar codes"
    - "how to construct polar codes"
    - "polar codes are optimal for lossy source coding"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"

Connected through hyperedge HE-2 (induced by source paper "secret key generation from gaussian sources using lattice hashing"):
  with members:
    - "semantically secure lattice codes for the gaussian wiretap channel"
    - "exponential decreasing rate of leaked information in universal random privacy amplification"
    - "secret key agreement from correlated gaussian sources by rate limited public communication"
    - "compute and forward harnessing interference through structured 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)