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 "dispersion bound for the wyner ahlswede k orner network via reverse hypercontractivity on types"):
  Abstract: Using the functional-entropic duality and the reverse hypercontractivity of the transposition semigroup, we lower bound the error probability for each joint type in the Wyner-Ahlswede-K\"orner problem. Then by averaging the error probability over types, we lower bound the $c$-dispersion (which characterizes the second-order behavior of the weighted sum of the rates of the two compressors when a nonvanishing error probability is small) as the variance of the gradient of $\inf_{P_{U|X}}\{cH(Y|U)+I(U;X)\}$ with respect to $Q_{XY}$, the per-letter side information and source distribution. On the other hand, using the method of types we derive a new upper bound on the $c$-dispersion, which improves the existing upper bounds but has a gap to the aforementioned lower bound.

Includes members:
  Member "strong converse using change of measure arguments":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "communication for generating correlation a unifying survey")
  Member "exponential strong converse for content identification with lossy recovery":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "exponential strong converse for successive refinement with causal decoder side information")
  Member "information theoretic perspectives on brascamp lieb inequality and its reverse":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "second order converses via reverse hypercontractivity")
  Member "a converse bound on wyner ahlswede k orner network via gray wyner network":
    also connected through other hyperedges:
      (none)
  Member "brascamp lieb inequality and its reverse an information theoretic view":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "capacity upper bounds for the relay channel via reverse hypercontractivity")
  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 "asymptotic estimates in information theory with non vanishing error probabilities":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "finite sample analysis of image registration")

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)