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: "lattice codes for many to one interference channels with and without cognitive messages"
  Abstract: A new achievable rate region is given for the Gaussian cognitive many-to-one interference channel. The proposed novel coding scheme is based on the compute-and-forward approach with lattice codes. Using the idea of decoding sums of codewords, our scheme improves considerably upon the conventional coding schemes which treat interference as noise or decode messages simultaneously. Our strategy also extends directly to the usual many-to-one interference channels without cognitive messages. Comparing to the usual compute-and-forward scheme where a fixed lattice is used for the code construction, the novel scheme employs scaled lattices and also encompasses key ingredients of the existing schemes for the cognitive interference channel. With this new component, our scheme achieves a larger rate region in general. For some symmetric channel settings, new constant gap or capacity results are established, which are independent of the number of users in the system.

Connected through hyperedge HE-1 (induced by source paper "generalized compute compress and forward"):
  with members:
    - "expanding the compute and forward framework unequal powers signal levels and multiple linear combinations"
    - "compute compress and forward exploiting asymmetry of wireless relay networks"
    - "asymmetric compute and forward with csit"
    - "optimized backhaul compression for uplink cloud radio access network"
    - "successive integer forcing and its sum rate optimality"
    - "an algebraic approach to physical layer network coding"
    - "integer forcing mimo linear receivers based on lattice reduction"
    - "a simple proof for the existence of good pairs of nested lattices"

Connected through hyperedge HE-2 (induced by source paper "on the sum capacity of many to one and one to many gaussian interference channels"):
  with members:
    - "on the gaussian many to one x channel"
    - "capacity bounds for the k user gaussian interference channel"
    - "k user interference channels general outer bound and sum capacity for certain gaussian channels"
    - "the approximate capacity of the many to one and one to many gaussian interference channels"
    - "on the capacity of the k user cyclic gaussian interference channel"
    - "interference alignment and a noisy interference regime for many to one interference channels"
    - "capacity bounds for the gaussian interference channel"
    - "new outer bounds on the capacity region of gaussian interference channels"

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)