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: "interference networks with point to point codes"
  Abstract: The paper establishes the capacity region of the Gaussian interference channel with many transmitter-receiver pairs constrained to use point-to-point codes. The capacity region is shown to be strictly larger in general than the achievable rate regions when treating interference as noise, using successive interference cancellation decoding, and using joint decoding. The gains in coverage and achievable rate using the optimal decoder are analyzed in terms of ensemble averages using stochastic geometry. In a spatial network where the nodes are distributed according to a Poisson point process and the channel path loss exponent is $\beta > 2$, it is shown that the density of users that can be supported by treating interference as noise can scale no faster than $B^{2/\beta}$ as the bandwidth $B$ grows, while the density of users can scale linearly with $B$ under optimal decoding.

Connected through hyperedge HE-1 (induced by source paper "increasing the throughput of an unlicensed wireless network through retransmissions"):
  with members:
    - "maximizing the link throughput between smart meters and aggregators as secondary users under power and outage constraints"
    - "on the joint impact of beamwidth and orientation error on throughput in directional wireless poisson networks"
    - "throughput analysis of cognitive wireless networks with poisson distributed nodes based on location information"
    - "an overview of the transmission capacity of wireless networks"

Connected through hyperedge HE-2 (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"

Connected through hyperedge HE-3 (induced by source paper "an information spectrum approach to the capacity region of general interference channel"):
  with members:
    - "accessible capacity of secondary users"
    - "error exponents of optimum decoding for the interference channel"
    - "gaussian interference channel capacity to within one bit"

Connected through hyperedge HE-4 (induced by source paper "simultaneous nonunique decoding is rate optimal"):
  with members:
    - "interference decoding for deterministic channels"

Connected through hyperedge HE-5 (induced by source paper "mmse of bad codes"):
  with members:
    - "soft decoding based strategies for relay and interference channels analysis and achievable rates using ldpc codes"
    - "communication with disturbance constraints"
    - "sub optimality of treating interference as noise in the cellular uplink"
    - "k user interference channels general outer bound and sum capacity for certain gaussian channels"
    - "statistical physics of signal estimation in gaussian noise theory and examples of phase transitions"
    - "estimation in gaussian noise properties of the minimum mean square error"
    - "on mmse properties and i mmse implications in parallel mimo gaussian channels"
    - "to decode the interference or to consider it as noise"

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)