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 "mismatched guesswork"):
  Abstract: We study the problem of mismatched guesswork, where we evaluate the number of symbols $y \in \mathcal{Y}$ which have higher likelihood than $X \sim \mu$ according to a mismatched distribution $\nu$. We discuss the role of the tilted/exponential families of the source distribution $\mu$ and of the mismatched distribution $\nu$. We show that the value of guesswork can be characterized using the tilted family of the mismatched distribution $\nu$, while the probability of guessing is characterized by an exponential family which passes through $\mu$. Using this characterization, we demonstrate that the mismatched guesswork follows a large deviation principle (LDP), where the rate function is described implicitly using information theoretic quantities. We apply these results to one-to-one source coding (without prefix free constraint) to obtain the cost of mismatch in terms of average codeword length. We show that the cost of mismatch in one-to-one codes is no larger than that of the prefix-free codes, i.e., $D(\mu\| \nu)$. Further, the cost of mismatch vanishes if and only if $\nu$ lies on the tilted family of the true distribution $\mu$, which is in stark contrast to the prefix-free codes. These results imply that one-to-one codes are inherently more robust to mismatch.

Includes members:
  Member "universal randomized guessing with application to asynchronous decentralized brute force attacks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "noisy guesses")
  Member "why botnets work distributed brute force attacks need no synchronization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "noisy guesses")
  Member "a characterization of guesswork on swiftly tilting curves":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fair resource allocation in federated learning")
  Member "centralized vs decentralized multi agent guesswork":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "universal randomized guessing with application to asynchronous decentralized brute force attacks")
  Member "guesswork subject to a total entropy budget":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "universal randomized guessing with application to asynchronous decentralized brute force attacks")
  Member "multi user guesswork and brute force security":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "capacity achieving guessing random additive noise decoding")
  Member "list decoding random coding exponents and expurgated exponents":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning an adversary s actions for secret communication")
  Member "asymptotics and non asymptotics for universal fixed to variable source coding":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)