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 "new strong converse for asymmetric broadcast channels"):
  Abstract: Here the set X corresponds to a channel input and the sets Y and Z correspond to two channel outputs. Let X be a random variable taking values in X. We write an element of X as x = x1x2· · ·xn. Suppose that X has a probability distribution on X denoted by pXn = {pXn(x)}xn∈Xn . Similar notations are adopted for other random variables. Let Y n ∈ Y and Z ∈ Y be random variables obtained as the channel output by connecting X to the input of channel. We write a conditional distribution of (Y , Z) on given X as W = {W(y, zn|xn)}(xn,yn,zn)∈Xn×Yn×Zn .

Includes members:
  Member "exponent function for one helper source coding problem at rates outside the rate region":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (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-3 (induced by source paper "strong converses are just edge removal properties")
  Member "error exponents for broadcast channels with degraded message sets":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "operational interpretation of renyi information measures via composite hypothesis testing against product and markov distributions")

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)