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: "remarks on the most informative function conjecture at fixed mean"
  Abstract: In 2013, Courtade and Kumar posed the following problem: Let $\boldsymbol{x} \sim \{\pm 1\}^n$ be uniformly random, and form $\boldsymbol{y} \sim \{\pm 1\}^n$ by negating each bit of $\boldsymbol{x}$ independently with probability $\alpha$. Is it true that the mutual information $I(f(\boldsymbol{x}) \mathbin{;} \boldsymbol{y})$ is maximized among $f:\{\pm 1\}^n \to \{\pm 1\}$ by $f(x) = x_1$? We do not resolve this problem. Instead, we make a couple of observations about the fixed-mean version of the conjecture. We show that Courtade and Kumar's stronger Lex Conjecture fails for small noise rates. We also prove a continuous version of the conjecture on the sphere and show that it implies the previously-known analogue for Gaussian space.

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"
    - "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 "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"
    - "euclidean information theory of networks"
    - "notes on information theoretic privacy"
    - "an improved upper bound for the most informative boolean function conjecture"

Connected through hyperedge HE-3 (induced by source paper "dictator functions maximize mutual information"):
  with members:
    - "an improved upper bound for the most informative boolean function conjecture"
    - "which boolean functions are most informative"
    - "canalizing boolean functions maximize the mutual information"

Connected through hyperedge HE-4 (induced by source paper "oblivious fronthaul constrained relay for a gaussian channel"):
  with members:
    - "fundamental distortion limits of analog to digital compression"
    - "distortion rate function of sub nyquist sampled gaussian sources"
    - "oblivious processing in a fronthaul constrained gaussian channel"
    - "lower bounds and approximations for the information rate of the isi channel"
    - "on the capacity of the dither quantized gaussian channel"
    - "shannon meets nyquist capacity of sampled gaussian channels"
    - "capacity of the discrete time awgn channel under output quantization"
    - "distributed mimo receiver achievable rates and upper bounds"

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)