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 "strong converse theorems for multimessage networks with tight cut set bound"):
  Abstract: This paper considers a multimessage network where each node may send a message to any other node in the network. Under the discrete memoryless model, we prove the strong converse theorem for any network with tight cut-set bound, i.e., whose cut-set bound is achievable. Our result implies that for any network with tight cut-set bound and any fixed rate vector that resides outside the capacity region, the average error probabilities of any sequence of length-$n$ codes operated at the rate vector must tend to $1$ as $n$ grows. The proof is based on the method of types. The proof techniques are inspired by the work of Csisz\'{a}r and K\"{o}rner in 1982 which fully characterized the reliability function of any discrete memoryless channel (DMC) with feedback for rates above capacity. In addition, we generalize the strong converse theorem to the Gaussian model where each node is subject to a peak power constraint. Important consequences of our results are new strong converses for the Gaussian multiple access channel (MAC) with feedback and the following relay channels under both models: The degraded relay channel (RC), the RC with orthogonal sender components, and the general RC with feedback.

Includes members:
  Member "new strong converse for asymmetric broadcast channels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "exponential strong converse for successive refinement with causal decoder side information")
  Member "achievable rates for gaussian degraded relay channels with non vanishing error probabilities":
    also connected through other hyperedges:
      (none)
  Member "strong converse theorems for classes of multimessage multicast networks a renyi divergence approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the renyi capacity and center")
  Member "on the reliability function of the discrete memoryless relay channel":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "achievable rates for gaussian degraded relay channels with non vanishing error probabilities")
  Member "on gaussian channels with feedback under expected power constraints and with non vanishing error probabilities":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the reliability function of the common message broadcast channel with variable length feedback")
  Member "strong converse exponent for degraded broadcast channels at rates outside the capacity region":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "second order converses via reverse hypercontractivity")
  Member "wireless network information flow a deterministic approach":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "best relay selection in gaussian half duplex diamond networks")
  Member "reliability in source coding with side information":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on binary distributed hypothesis testing")

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)