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 couplings"
  Abstract: Abstract   In this paper, some general properties of Shannon information measures are investigated over sets of probability distributions with restricted marginals. Certain optimization problems associated with these functionals are shown to be NP-hard, and their special cases are found to be essentially information-theoretic restatements of well-known computational problems, such as the  Subset sum  and the  3-Partition . The notion of minimum entropy coupling is introduced and its relevance is demonstrated in information-theoretic, computational, and statistical contexts. Finally, a family of pseudometrics (on the space of discrete probability distributions) defined by these couplings is studied, in particular their relation to the total variation distance, and a new characterization of the conditional entropy is given.

Connected through hyperedge HE-1 (induced by source paper "minimum entropy couplings and their applications"):
  with members:
    - "strong functional representation lemma and applications to coding theorems"
    - "bounds on the entropy of a function of a random variable and their applications"
    - "mutual information bounds via adjacency events"
    - "entropic causal inference"
    - "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-2 (induced by source paper "asymptotic coupling and its applications in information theory"):
  with members:
    - "divergence scaling of fixed length binary output one to one distribution matching"
    - "measuring secrecy by the probability of a successful guess"
    - "fundamental limits of communication with low probability of detection"
    - "distributed simulation of continuous random variables"
    - "covert communication over noisy channels a resolvability perspective"
    - "generalized common information common information extraction and private sources synthesis"
    - "source channel secrecy for shannon cipher system"
    - "quantum secure covert communication on bosonic channels"

Connected through hyperedge HE-3 (induced by source paper "playing games with bounded entropy"):
  with members:
    - "how to find a joint probability distribution of minimum entropy almost given the marginals"
    - "entropic causal inference"
    - "on the hardness of entropy minimization and related problems"
    - "repeated matching pennies with limited randomness"

Connected through hyperedge HE-4 (induced by source paper "bounds on the entropy of a function of a random variable and their applications"):
  with members:
    - "hard clusters maximize mutual information"
    - "fast algorithms for constructing maximum entropy summary trees"
    - "a characterization of entropy in terms of information loss"
    - "quantization of binary input discrete memoryless channels"

Connected through hyperedge HE-5 (induced by source paper "h x vs h f x"):
  with members:
    - "hard clusters maximize mutual information"
    - "a characterization of entropy in terms of information loss"
    - "quantization of binary input discrete memoryless channels"

Connected through hyperedge HE-6 (induced by source paper "how to find a joint probability distribution of minimum entropy almost given the marginals"):
  with members:
    - "entropic causal inference"
    - "a metric between probability distributions on finite sets of different cardinalities and applications to order reduction"

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)