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 converses are just edge removal properties"):
  Abstract: This paper explores the relationship between two ideas in network information theory: edge removal and strong converses. Edge removal properties state that if an edge of small capacity is removed from a network, the capacity region does not change too much. Strong converses state that, for rates outside the capacity region, the probability of error converges to 1 as the blocklength goes to infinity. Various notions of edge removal and strong converse are defined, depending on how edge capacity and error probability scale with blocklength, and relations between them are proved. Each class of strong converse implies a specific class of edge removal. The opposite directions are proved for deterministic networks. Furthermore, a technique based on a novel, causal version of the blowing-up lemma is used to prove that for discrete memoryless networks, the weak edge removal property--that the capacity region changes continuously as the capacity of an edge vanishes--is equivalent to the exponentially strong converse--that outside the capacity region, the probability of error goes to 1 exponentially fast. This result is used to prove exponentially strong converses for several examples, including the discrete 2-user interference channel with strong interference, with only a small variation from traditional weak converse proofs.

Includes members:
  Member "on the impact of a single edge on the network coding capacity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a local perspective on the edge removal problem")
  Member "on the capacity advantage of a single bit":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "negligible cooperation contrasting the maximal and average error cases")
  Member "smoothing brascamp lieb inequalities and strong converses for common randomness generation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "resolution limits of noisy 20 questions estimation")
  Member "a proof of the strong converse theorem for gaussian multiple access channels":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "gaussian multiple and random access in the finite blocklength regime")
  Member "exponent function for one helper source coding problem at rates outside the rate region":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "exponential strong converse for successive refinement with causal decoder side information")
  Member "strong converse exponent for degraded broadcast channels at rates outside the capacity region":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "second order converses via reverse hypercontractivity")
  Member "strong converse theorems for degraded broadcast channels with feedback":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "exponent function for stationary memoryless channels with input cost at rates above the capacity")
  Member "equivalence for networks with adversarial state":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adversarial network coding")

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)