human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "negligible cooperation contrasting the maximal and average error cases"):
  with members:
    - "on the impact of a single edge on the network coding capacity"
    - "on the capacity advantage of a single bit"
    - "fundamental limits of communication with low probability of detection"
    - "network coding is zero error always possible"

Connected through hyperedge HE-2 (induced by source paper "strong converse using change of measure arguments"):
  with members:
    - "a new proof of nonsignalling multiprover parallel repetition theorem"
    - "communication for generating correlation a survey"
    - "communication for generating correlation a unifying survey"
    - "improved finite blocklength converses for slepian wolf coding via linear programming"
    - "second order region for gray wyner network"
    - "discrete lossy gray wyner revisited second order asymptotics large and moderate deviations"
    - "wiretap channel capacity secrecy criteria strong converse and phase change"
    - "linear programming based converses for finite blocklength lossy joint source channel coding"

Connected through hyperedge HE-3 (induced by source paper "can negligible cooperation increase network capacity the average error case"):
  with members:
    - "on the impact of a single edge on the network coding capacity"
    - "on the capacity advantage of a single bit"
    - "network coding is zero error always possible"

Question: Please tell me which class the center node 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)