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: "fourier motzkin elimination software for information theoretic inequalities"
  Abstract: We provide open-source software implemented in MATLAB, that performs Fourier-Motzkin elimination (FME) and removes constraints that are redundant due to Shannon-type inequalities (STIs). The FME is often used in information theoretic contexts to simplify rate regions, e.g., by eliminating auxiliary rates. Occasionally, however, the procedure becomes cumbersome, which makes an error-free hand-written derivation an elusive task. Some computer software have circumvented this difficulty by exploiting an automated FME process. However, the outputs of such software often include constraints that are inactive due to information theoretic properties. By incorporating the notion of STIs (a class of information inequalities provable via a computer program), our algorithm removes such redundant constraints based on non-negativity properties, chain-rules and probability mass function factorization. This newsletter first illustrates the program's abilities, and then reviews the contribution of STIs to the identification of redundant constraints.

Connected through hyperedge HE-1 (induced by source paper "secrecy rates for a channel with two senders and two receivers"):
  with members:
    - "energy efficient multicell multigroup multicasting with joint beamforming and antenna selection"
    - "secure multi source multicast"
    - "semantic security capacity for wiretap channels of type ii"
    - "secrecy capacity region of some classes of wiretap broadcast channels"
    - "strong secrecy for multiple access channels"
    - "distributed channel synthesis"
    - "achievability proof via output statistics of random binning"
    - "multi receiver wiretap channel with public and confidential messages"

Connected through hyperedge HE-2 (induced by source paper "broadcast channels with privacy leakage constraints"):
  with members:
    - "the likelihood encoder for lossy compression"
    - "wiretap channels with random states non causally available at the encoder"
    - "strong secrecy for cooperative broadcast channels"
    - "semantic security capacity for wiretap channels of type ii"
    - "secrecy capacity region of some classes of wiretap broadcast channels"
    - "a technique for deriving one shot achievability results in network information theory"
    - "effective secrecy reliability confusion and stealth"
    - "the capacity region of the two receiver vector gaussian broadcast channel with private and common messages"

Connected through hyperedge HE-3 (induced by source paper "cooperative binning for semi deterministic channels with non causal state information"):
  with members:
    - "cooperative binning for semideterministic channels"
    - "capacity of a class of state dependent orthogonal relay channels"
    - "multiple access channels with combined cooperation and partial cribbing"
    - "bounds on the capacity of the relay channel with noncausal state at source"
    - "two way source coding with a helper"

Connected through hyperedge HE-4 (induced by source paper "strong secrecy for cooperative broadcast channels"):
  with members:
    - "broadcast channels with privacy leakage constraints"
    - "the likelihood encoder for lossy compression"
    - "duality of a source coding problem and the semi deterministic broadcast channel with rate limited cooperation"
    - "semantic security capacity for wiretap channels of type ii"
    - "secrecy capacity region of some classes of wiretap broadcast channels"
    - "on state dependent broadcast channels with cooperation"
    - "the henchman problem measuring secrecy by the minimum distortion in a list"
    - "secure coordination with a two sided helper"

Connected through hyperedge HE-5 (induced by source paper "on achievability for downlink cloud radio access networks with base station cooperation"):
  with members:
    - "recent advances in cloud radio access networks system architectures key techniques and open issues"
    - "capacity bounds for diamond networks with an orthogonal broadcast channel"
    - "cloud radio access network virtualizing wireless access for dense heterogeneous systems"
    - "distributed decode forward for relay networks"
    - "sparse beamforming and user centric clustering for downlink cloud radio access network"
    - "joint precoding and multivariate backhaul compression for the downlink of cloud radio access networks"
    - "compute and forward strategies for cooperative distributed antenna systems"
    - "the gaussian multiple access diamond channel"

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)