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: "an improved upper bound for the most informative boolean function conjecture"
  Abstract: Suppose $X$ is a uniformly distributed $n$-dimensional binary vector and $Y$ is obtained by passing $X$ through a binary symmetric channel with crossover probability $\alpha$. A recent conjecture by Courtade and Kumar postulates that $I(f(X);Y)\leq 1-h(\alpha)$ for any Boolean function $f$. So far, the best known upper bound was $I(f(X);Y)\leq (1-2\alpha)^2$. In this paper, we derive a new upper bound that holds for all balanced functions, and improves upon the best known bound for all $\tfrac{1}{3}<\alpha<\tfrac{1}{2}$.

Connected through hyperedge HE-1 (induced by source paper "guessing with a bit of help"):
  with members:
    - "reducing guesswork via an unreliable oracle"
    - "on the optimal boolean function for prediction under quadratic loss"
    - "password cracking the effect of hash function bias on the average guesswork"
    - "on the entropy of a noisy function"
    - "remarks on the most informative function conjecture at fixed mean"
    - "multi user guesswork and brute force security"
    - "analysis of boolean functions"
    - "strong data processing inequalities and phi sobolev inequalities for discrete channels"

Connected through hyperedge HE-2 (induced by source paper "on the most informative boolean functions of the very noisy channel"):
  with members:
    - "how to quantize n outputs of a binary symmetric channel to n 1 bits"
    - "on the entropy of a noisy function"

Connected through hyperedge HE-3 (induced by source paper "principal inertia components and applications"):
  with members:
    - "brascamp lieb inequality and its reverse an information theoretic view"
    - "the most informative boolean function conjecture holds for high noise"
    - "bounds between contraction coefficients"
    - "on the duality of additivity and tensorization"
    - "managing your private and public data bringing down inference attacks against your privacy"
    - "remarks on the most informative function conjecture at fixed mean"
    - "euclidean information theory of networks"
    - "notes on information theoretic privacy"

Connected through hyperedge HE-4 (induced by source paper "how to quantize n outputs of a binary symmetric channel to n 1 bits"):
  with members:
    - "on the optimal boolean function for prediction under quadratic loss"
    - "on the entropy of a noisy function"

Connected through hyperedge HE-5 (induced by source paper "on the optimal boolean function for prediction under quadratic loss"):
  with members:
    - "the most informative boolean function conjecture holds for high noise"
    - "on the entropy of a noisy function"
    - "analysis of boolean functions"
    - "an exploration of the role of principal inertia components in information theory"

Connected through hyperedge HE-6 (induced by source paper "the maximum mutual information between the output of a binary symmetric channel and a boolean function of its input"):
  with members:
    - "the most informative boolean function conjecture holds for high noise"
    - "on the entropy of a noisy function"
    - "an exploration of the role of principal inertia components in information theory"

Connected through hyperedge HE-7 (induced by source paper "dictator functions maximize mutual information"):
  with members:
    - "remarks on the most informative function conjecture at fixed mean"
    - "which boolean functions are most informative"
    - "canalizing boolean functions maximize the mutual information"

Connected through hyperedge HE-8 (induced by source paper "on the entropy of a noisy function"):
  with members:
    - "strong data processing inequalities for channels and bayesian networks"
    - "novel lower bounds on the entropy rate of binary hidden markov processes"
    - "dissipation of information in channels with input constraints"
    - "analysis of boolean functions"

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)