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: "two user gaussian interference channel with finite constellation input and fdma"
  Abstract: In the two-user Gaussian Strong Interference Channel (GSIC) with finite constellation inputs, it is known that relative rotation between the constellations of the two users enlarges the Constellation Constrained (CC) capacity region. In this paper, a metric for finding the approximate angle of rotation (with negligibly small error) to maximally enlarge the CC capacity for the two-user GSIC is presented. In the case of Gaussian input alphabets with equal powers for both the users and the modulus of both the cross-channel gains being equal to unity, it is known that the FDMA rate curve touches the capacity curve of the GSIC. It is shown that, with unequal powers for both the users also, when the modulus of one of the cross-channel gains being equal to one and the modulus of the other cross-channel gain being greater than or equal to one, the FDMA rate curve touches the capacity curve of the GSIC. On the contrary, it is shown that, under finite constellation inputs, with both the users using the same constellation, the FDMA rate curve strictly lies within (never touches) the enlarged CC capacity region throughout the strong-interference regime. This means that using FDMA it is impossible to go close to the CC capacity. It is well known that for the Gaussian input alphabets, the FDMA inner-bound, at the optimum sum-rate point, is always better than the simultaneous-decoding inner-bound throughout the weak-interference regime. For a portion of the weak interference regime, it is shown that with identical finite constellation inputs for both the users, the simultaneous-decoding inner-bound, enlarged by relative rotation between the constellations, is strictly better than the FDMA inner-bound.

Connected through hyperedge HE-1 (induced by source paper "on precoding for constant k user mimo gaussian interference channel with finite constellation inputs"):
  with members:
    - "real interference alignment exploiting the potential of single antenna systems"
    - "linear precoder design for mimo interference channels with finite alphabet signaling"
    - "interference alignment for the mimo interference channel with delayed local csit"
    - "achievable rates in two user interference channels with finite inputs and very strong interference"
    - "interference alignment for the k user mimo interference channel"
    - "capacity regions and sum rate capacities of vector gaussian interference channels"
    - "linear transceiver design for interference alignment complexity and computation"
    - "mimo precoding with x and y codes"

Connected through hyperedge HE-2 (induced by source paper "capacity region of k user discrete memoryless interference channels with a mixed strong very strong interference"):
  with members:
    - "achievable rates in two user interference channels with finite inputs and very strong interference"
    - "lecture notes on network information theory"
    - "the capacity region of the 3 user gaussian interference channel with mixed strong very strong interference"
    - "a new outer bound and the noisy interference sum rate capacity for gaussian interference channels"
    - "capacity of symmetric k user gaussian very strong interference channels"
    - "gaussian interference channel capacity to within one bit"

Connected through hyperedge HE-3 (induced by source paper "bounds on the achievable rate for the fading relay channel with finite input constellations"):
  with members:
    - "secrecy capacity of the gaussian wire tap channel with finite complex constellation input"

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)