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: "on the corner points of the capacity region of a two user gaussian interference channel"
  Abstract: This work considers the corner points of the capacity region of a two-user Gaussian interference channel (GIC). In a two-user GIC, the rate pairs where one user transmits its data at the single-user capacity (without interference), and the other at the largest rate for which reliable communication is still possible are called corner points. This paper relies on existing outer bounds on the capacity region of a two-user GIC that are used to derive informative bounds on the corner points of the capacity region. The new bounds refer to a weak two-user GIC (i.e., when both cross-link gains in standard form are positive and below 1), and a refinement of these bounds is obtained for the case where the transmission rate of one user is within $\varepsilon > 0$ of the single-user capacity. The bounds on the corner points are asymptotically tight as the transmitted powers tend to infinity, and they are also useful for the case of moderate SNR and INR. Upper and lower bounds on the gap (denoted by $\Delta$) between the sum-rate and the maximal achievable total rate at the two corner points are derived. This is followed by an asymptotic analysis analogous to the study of the generalized degrees of freedom (where the SNR and INR scalings are coupled such that $\frac{\log(\text{INR})}{\log(\text{SNR})} = \alpha \geq 0$), leading to an asymptotic characterization of this gap which is exact for the whole range of $\alpha$. The upper and lower bounds on $\Delta$ are asymptotically tight in the sense that they achieve the exact asymptotic characterization. Improved bounds on $\Delta$ are derived for finite SNR and INR, and their improved tightness is exemplified numerically.

Connected through hyperedge HE-1 (induced by source paper "at every corner determining corner points of two user gaussian interference channels"):
  with members:
    - "yet another proof of the entropy power inequality"
    - "wasserstein continuity of entropy and outer bounds for interference channels"
    - "the effect of maximal rate codes on the interfering message rate"
    - "information theoretic proofs of entropy power inequalities"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-2 (induced by source paper "optimal point to point codes in interference channels an incremental i mmse approach"):
  with members:
    - "wasserstein continuity of entropy and outer bounds for interference channels"
    - "the effect of maximal rate codes on the interfering message rate"
    - "estimation in gaussian noise properties of the minimum mean square error"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-3 (induced by source paper "wasserstein continuity of entropy and outer bounds for interference channels"):
  with members:
    - "optimal point to point codes in interference channels an incremental i mmse approach"
    - "dissipation of information in channels with input constraints"
    - "the effect of maximal rate codes on the interfering message rate"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-4 (induced by source paper "codewords with memory improve achievable rate regions of the memoryless gaussian interference channel"):
  with members:
    - "wasserstein continuity of entropy and outer bounds for interference channels"
    - "the effect of maximal rate codes on the interfering message rate"

Connected through hyperedge HE-5 (induced by source paper "the effect of maximal rate codes on the interfering message rate"):
  with members:
    - "on mmse properties of codes for the gaussian broadcast and wiretap channels"
    - "sub optimality of the han kobayashi achievable region for interference channels"
    - "random coding error exponents for the two user interference channel"
    - "wasserstein continuity of entropy and outer bounds for interference channels"
    - "on the two user interference channel with lack of knowledge of the interference codebook at one receiver"
    - "sub optimality of treating interference as noise in the cellular uplink with weak interference"
    - "on discrete alphabets for the two user gaussian interference channel with one receiver lacking knowledge of the interfering codebook"
    - "on the optimality of treating interference as noise general message sets"

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)