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 "on the bee identification error exponent with absentee bees"):
  Abstract: The "bee-identification problem" was formally defined by Tandon, Tan and Varshney [IEEE Trans. Commun. (2019) [Online early access]], and the error exponent was studied. This work extends the results for the "absentee bees" scenario, where a small fraction of the bees are absent in the beehive image used for identification. For this setting, we present an exact characterization of the bee-identification error exponent, and show that independent barcode decoding is optimal, i.e., joint decoding of the bee barcodes does not result in a better error exponent relative to independent decoding of each noisy barcode. This is in contrast to the result without absentee bees, where joint barcode decoding results in a significantly higher error exponent than independent barcode decoding. We also define and characterize the "capacity" for the bee-identification problem with absentee bees, and prove the strong converse for the same.

Includes members:
  Member "capacity results for the noisy shuffling channel":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dna based storage models and fundamental limits")
  Member "on identifying a massive number of distributions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "error exponent bounds for the bee identification problem")
  Member "codes in the space of multisets coding for permutation channels with impairments":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimal constant weight codes under l_1 metric")
  Member "fundamental limits of dna storage systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dna based storage models and fundamental limits")
  Member "universal decoding for arbitrary channels relative to a given class of decoding metrics":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gaussian intersymbol interference channels with mismatch")
  Member "distance distribution of binary codes and the error probability of decoding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adaptive causal network coding with feedback")

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)