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 "the k user dm broadcast channel with two groupcast messages achievable rate regions and the combination network as a case study"):
  Abstract: A novel class of achievable rate regions is obtained for the K-receiver broadcast channel with two groupcast messages. The associated achievability schemes are parameterized by an expansion of the message set which then determines how random coding techniques are employed, which include generalized versions of {\em up-set} message-splitting, the generation of possibly multiple auxiliary codebooks for certain compositions of split messages using superposition coding, partial interference decoding at all receivers, and joint unique and non-unique decoding. #R##N#New capacity results are established for certain partially ordered classes of general broadcast channels for certain two non-nested messages. Moreover, when specialized to the combination network (CN), some of the inner bounds are shown, via converse results, to result in the capacity region for (a) the two messages intended for two sets of K-1 receivers each and (b) two nested messages in which one message is intended for one or (c) two (common) receivers. In the latter two cases, we hence recover previous results by Bidokhti et al obtained therein using network coding schemes based on rate-splitting and linear superposition coding. Furthermore, we show the achievability of rate pairs in two examples of CNs, with three and four common receivers each, used in the previous literature to show the sub-optimality of rate-splitting and linear superposition coding, and to motivate a pre-encoding technique and a block-Markov linear superposition coding for the CN, with the latter then lifted to the general broadcast channel. Our results suggest that the proposed framework here, when specialized to the CN, is strong enough to incorporate the enhancements afforded by those two latter techniques, thereby suggesting among other things, that perhaps block-Markov superposition coding is not necessary for the general broadcast channel.

Includes members:
  Member "noisy broadcast networks with receiver caching":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fundamental limits of wireless caching under mixed cacheable and uncacheable traffic")
  Member "capacity results for multicasting nested message sets over combination networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an achievable rate region for two groupcast messages over the k user broadcast channel and capacity results for the combination network")
  Member "is non unique decoding necessary":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "harnessing correlations in distributed erasure coded key value stores")
  Member "generalized cut set bounds for broadcast networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an achievable rate region for two groupcast messages over the k user broadcast channel and capacity results for the combination network")
  Member "latent capacity region a case study on symmetric broadcast with common messages":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an achievable rate region for two groupcast messages over the k user broadcast channel and capacity results for the combination network")
  Member "fundamental constraints on multicast capacity regions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an achievable rate region for two groupcast messages over the k user broadcast channel and capacity results for the combination network")

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)