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: "on the entropy of a noisy function"
  Abstract: Let $0   0$. Namely, if $X$ is uniformly distributed in $\{0,1\}^n$ and $Y$ is obtained by flipping each coordinate of $X$ independently with probability $\epsilon$, then, provided $\epsilon \ge 1/2 - \delta$, for any boolean function $f$ holds $I\Big(f(X);Y\Big) \le 1 - H(\epsilon)$.

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"
    - "remarks on the most informative function conjecture at fixed mean"
    - "multi user guesswork and brute force security"
    - "an improved upper bound for the most informative boolean function conjecture"
    - "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"
    - "an improved upper bound for the most informative boolean function conjecture"

Connected through hyperedge HE-3 (induced by source paper "an upper bound on ell_q norms of noisy functions"):
  with members:
    - "analysis of boolean functions"
    - "reed muller codes for random erasures and errors"

Connected through hyperedge HE-4 (induced by source paper "strong data processing inequalities for channels and bayesian networks"):
  with members:
    - "novel lower bounds on the entropy rate of binary hidden markov processes"
    - "converses for distributed estimation via strong data processing inequalities"
    - "strong data processing inequalities for input constrained additive noise channels"
    - "dissipation of information in channels with input constraints"
    - "strong data processing inequalities and phi sobolev inequalities for discrete channels"
    - "tractable inference for complex stochastic processes"
    - "on maximal correlation hypercontractivity and the data processing inequality studied by erkip and cover"
    - "on pairs of f divergences and their joint range"

Connected through hyperedge HE-5 (induced by source paper "on the courtade kumar conjecture for certain classes of boolean functions"):
  with members:
    - "an exploration of the role of principal inertia components in information theory"
    - "which boolean functions are most informative"

Connected through hyperedge HE-6 (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"
    - "an improved upper bound for the most informative boolean function conjecture"

Connected through hyperedge HE-7 (induced by source paper "the maximum mutual information between the output of a discrete symmetric channel and several classes of boolean functions of its input"):
  with members:
    - "an exploration of the role of principal inertia components in information theory"

Connected through hyperedge HE-8 (induced by source paper "novel lower bounds on the entropy rate of binary hidden markov processes"):
  with members:
    - "strong data processing inequalities for channels and bayesian networks"
    - "minimum ms e gerber s lemma"
    - "entropy rate for hidden markov chains with rare transitions"
    - "the capacity of finite state channels in the high noise regime"
    - "derivatives of entropy rate in special families of hidden markov chains"

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)