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: "communicating the difference of correlated gaussian sources over a mac"
  Abstract: This paper considers the problem of transmitting the difference of two jointly Gaussian sources over a two-user additive Gaussian noise multiple access channel (MAC). The goal is to recover this difference within an average mean squared error distortion criterion. Each transmitter has access to only one of the two Gaussian sources and is limited by an average power constraint. In this work, a lattice coding scheme that achieves a distortion within a constant of a distortion lower bound is presented if the signal to noise ratio (SNR) is greater than a threshold. Further, uncoded transmission is shown to be worse in performance to lattice coding methods. An alternative lattice coding scheme is presented that can potentially improve on the performance of uncoded transmission.

Connected through hyperedge HE-1 (induced by source paper "compute and forward harnessing interference through structured codes"):
  with members:
    - "real interference alignment exploiting the potential of single antenna systems"
    - "an algebraic approach to physical layer network coding"
    - "wireless network information flow a deterministic approach"
    - "reliable physical layer network coding"
    - "the degrees of freedom of compute and forward"
    - "nested lattice codes for gaussian relay networks with interference"
    - "physical layer network coding tutorial survey and beyond"
    - "integer forcing linear receivers"

Connected through hyperedge HE-2 (induced by source paper "distributed joint source channel coding for functions over a multiple access channel"):
  with members:
    - "distributed joint source channel coding on a multiple access channel with side information"
    - "lattices for distributed source coding jointly gaussian sources and reconstruction of a linear function"
    - "sending a bi variate gaussian source over a gaussian mac"
    - "sending a bi variate gaussian source over a gaussian mac"

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)