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 "a correlation measure based on vector valued l_p norms"):
  Abstract: In this paper, we introduce a new measure of correlation for bipartite quantum states. This measure depends on a parameter $\alpha$, and is defined in terms of vector-valued $L_p$-norms. The measure is within a constant of the exponential of $\alpha$-R\'enyi mutual information, and reduces to the trace norm (total variation distance) for $\alpha=1$. We will prove some decoupling type theorems in terms of this measure of correlation, and present some applications in privacy amplification as well as in bounding the random coding exponents. In particular, we establish a bound on the secrecy exponent of the wiretap channel (under the total variation metric) in terms of the $\alpha$-R\'enyi mutual information according to \emph{Csisz\'ar's proposal}.

Includes members:
  Member "exact exponent for soft covering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "communication for generating correlation a unifying survey")
  Member "exact soft covering exponent":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "communication for generating correlation a survey")
  Member "generalizing bottleneck problems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "obfuscation via information density estimation")
  Member "measuring secrecy by the probability of a successful guess":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an operational approach to information leakage")
  Member "r enyi resolvability and its applications to the wiretap channel":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "resolvability on continuous alphabets")
  Member "exact random coding secrecy exponents for the wiretap channel":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "communication for generating correlation a unifying survey")
  Member "a large deviations approach to secure lossy compression":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "importance of small probability events in big data information measures applications and challenges")
  Member "f divergence inequalities":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on implicit regularization in beta vaes")

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)