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: "bounds on the entropy of a function of a random variable and their applications"
  Abstract: It is well known that the entropy $H(X)$ of a discrete random variable $X$ is always greater than or equal to the entropy $H(f(X))$ of a function $f$ of $X$, with equality if and only if $f$ is one-to-one. In this paper, we give tight bounds on $H(f(X))$ when the function $f$ is not one-to-one, and we illustrate a few scenarios where this matters. As an intermediate step towards our main result, we derive a lower bound on the entropy of a probability distribution, when only a bound on the ratio between the maximal and minimal probabilities is known. The lower bound improves on previous results in the literature, and it could find applications outside the present scenario.

Connected through hyperedge HE-1 (induced by source paper "on data processing and majorization inequalities for f divergences with applications"):
  with members:
    - "minimum entropy couplings and their applications"
    - "on f divergences integral representations local behavior and inequalities"
    - "comparison of channels criteria for domination by a symmetric channel"
    - "strong data processing inequalities for channels and bayesian networks"
    - "f divergence inequalities"
    - "e_ gamma resolvability"
    - "bounds between contraction coefficients"
    - "strong data processing inequalities for input constrained additive noise channels"

Connected through hyperedge HE-2 (induced by source paper "minimum entropy couplings and their applications"):
  with members:
    - "strong functional representation lemma and applications to coding theorems"
    - "mutual information bounds via adjacency events"
    - "entropic causal inference"
    - "on the entropy of couplings"
    - "sumset and inverse sumset inequalities for differential entropy and mutual information"
    - "entropic inequalities and marginal problems"
    - "entropy bounds for discrete random variables via maximal coupling"
    - "a metric between probability distributions on finite sets of different cardinalities and applications to order reduction"

Connected through hyperedge HE-3 (induced by source paper "categorical feature compression via submodular optimization"):
  with members:
    - "tensorflow a system for large scale machine learning"
    - "the power of randomization distributed submodular maximization on massive datasets"
    - "randomized composable core sets for distributed submodular maximization"
    - "adam a method for stochastic optimization"
    - "lazier than lazy greedy"
    - "quantization of binary input discrete memoryless channels"

Connected through hyperedge HE-4 (induced by source paper "on weighted uncertainty sampling in active learning"):
  with members:
    - "on the relationship between data efficiency and error for uncertainty sampling"
    - "identifying unknown unknowns in the open world representations and policies for guided exploration"

Connected through hyperedge HE-5 (induced by source paper "tight bounds on the r enyi entropy via majorization with applications to guessing and compression"):
  with members:
    - "on the conditional smooth renyi entropy and its applications in guessing and source coding"
    - "maximum entropy interval aggregations"
    - "distributed task encoding"
    - "reducing guesswork via an unreliable oracle"
    - "guessing attacks on distributed storage systems"
    - "on the renyi divergence joint range of relative entropies and a channel coding theorem"
    - "on the smooth renyi entropy and variable length source coding allowing errors"
    - "encoding tasks and renyi entropy"

Connected through hyperedge HE-6 (induced by source paper "information distilling quantizers"):
  with members:
    - "strong data processing inequalities for channels and bayesian networks"
    - "opening the black box of deep neural networks via information"
    - "greedy merge degrading has optimal power law"
    - "on the construction of polar codes for channels with moderate input alphabet sizes"
    - "justification of logarithmic loss via the benefit of side information"
    - "deep learning and the information bottleneck principle"
    - "on maximal correlation hypercontractivity and the data processing inequality studied by erkip and cover"
    - "on the construction of polar codes"

Connected through hyperedge HE-7 (induced by source paper "maximum entropy interval aggregations"):
  with members:
    - "hard clusters maximize mutual information"
    - "quantization of binary input discrete memoryless channels"

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)