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 hardness of entropy minimization and related problems"
  Abstract: We investigate certain optimization problems for Shannon information measures, namely, minimization of joint and conditional entropies H(X, Y), H(X|Y), H(Y|X), and maximization of mutual information I(X; Y), over convex regions. When restricted to the so-called transportation polytopes (sets of distributions with fixed marginals), very simple proofs of NP-hardness are obtained for these problems because in that case they are all equivalent, and their connection to the well-known SUBSET SUM and PARTITION problems is revealed. The computational intractability of the more general problems over arbitrary polytopes is then a simple consequence. Further, a simple class of polytopes is shown over which the above problems are not equivalent and their complexity differs sharply, namely, minimization of H(X, Y) and H(Y|X) is trivial, while minimization of H(X|Y) and maximization of I(X; Y) are strongly NP-hard problems. Finally, two new (pseudo)metrics on the space of discrete probability distributions are introduced, based on the so-called variation of information quantity, and NP-hardness of their computation is shown.

Connected through hyperedge HE-1 (induced by source paper "tropical probability theory and an application to the entropic cone"):
  with members:
    - "non shannon information inequalities in four random variables"

Connected through hyperedge HE-2 (induced by source paper "innovation representation of stochastic processes with application to causal inference"):
  with members:
    - "on the universality of the logistic loss function"
    - "lossless and lossy compression of random forests"
    - "bregman divergence bounds and the universality of the logarithmic loss"
    - "entropic causal inference"
    - "large alphabet source coding using independent component analysis"
    - "learning causal graphs with small interventions"
    - "generalized independent component analysis over finite alphabets"
    - "distinguishing cause from effect using observational data methods and benchmarks"

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 entropy of couplings"
    - "repeated matching pennies with limited randomness"

Connected through hyperedge HE-4 (induced by source paper "entropic causality and greedy minimum entropy coupling"):
  with members:
    - "directed information graphs"

Connected through hyperedge HE-5 (induced by source paper "entropic causal inference"):
  with members:
    - "distinguishing cause from effect using observational data methods and benchmarks"
    - "directed information graphs"

Connected through hyperedge HE-6 (induced by source paper "on the entropy of couplings"):
  with members:
    - "some properties of renyi entropy over countably infinite alphabets"
    - "entropy bounds for discrete random variables via maximal coupling"
    - "minimum entropy combinatorial optimization problems"
    - "finding the maximizers of the information divergence from an exponential family"
    - "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)