human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "fundamental limits of communications in interference networks part ii information flow in degraded networks"):
  Abstract: In this second part of our multi-part papers, the information flow in degraded interference networks is studied. A full characterization of the sum-rate capacity for the degraded networks with any possible configuration is established. It is shown that a successive decoding scheme is sum-rate optimal for these networks. Also, it is proved that the transmission of only a certain subset of messages is sufficient to achieve the sum-rate capacity in such networks. Algorithms are presented to determine this subset of messages explicitly. According to these algorithms, the optimal strategy to achieve the sum-rate capacity in degraded networks is that the transmitters try to send information for the stronger receivers and, if possible, avoid sending the messages with respect to the weaker receivers. The algorithms are easily understood using our graphical illustrations for the achievability schemes based on directed graphs. The sum-rate expression for the degraded networks is then used to derive a unified outer bound on the sum-rate capacity of arbitrary non-degraded networks. Several variations of the degraded networks are identified for which the derived outer bound is sum-rate optimal. Specifically, noisy interference regimes are derived for certain classes of multi-user/multi-message interference networks. Also, for the first time, network scenarios are identified where the incorporation of both successive decoding and treating interference as noise achieves their sum-rate capacity. Finally, by taking insight from our results for degraded networks, we establish a unified outer bound on the entire capacity region of the general interference networks. These outer bounds for a broad range of network scenarios are tighter than the existing cut-set bound.

Includes members:
  Member "fundamental limits of communications in interference networks part i basic structures":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the capacity region of the wireless ergodic fading interference channel with partial csit to within one bit")
  Member "interference networks with general message sets a random coding scheme":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "capacity bounds for wireless ergodic fading broadcast channels with partial csit")
  Member "the approximate capacity of the many to one and one to many gaussian interference channels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fundamental limits of wireless caching under mixed cacheable and uncacheable traffic")
  Member "sum capacity of k user gaussian degraded interference channels":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "capacity bounds for the k user gaussian interference channel")
  Member "lecture notes on network information theory":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the required number of antennas in a point to point large but finite mimo system outage limited scenario")

Question: Please tell me which class the source paper 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)