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: "a characterization of entropy in terms of information loss"
  Abstract: There are numerous characterizations of Shannon entropy and Tsallis entropy as measures of information obeying certain properties. Using work by Faddeev and Furuichi, we derive a very simple characterization. Instead of focusing on the entropy of a probability measure on a finite set, this characterization focuses on the "information loss", or change in entropy, associated with a measure-preserving function. Information loss is a special case of conditional entropy: namely, it is the entropy of a random variable conditioned on some function of that variable. We show that Shannon entropy gives the only concept of information loss that is functorial, convex-linear and continuous. This characterization naturally generalizes to Tsallis entropy as well.

Connected through hyperedge HE-1 (induced by source paper "gamma spaces and information"):
  with members:
    - "a bayesian characterization of relative entropy"

Connected through hyperedge HE-2 (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"
    - "on the entropy of couplings"
    - "fast algorithms for constructing maximum entropy summary trees"
    - "quantization of binary input discrete memoryless channels"

Connected through hyperedge HE-3 (induced by source paper "h x vs h f x"):
  with members:
    - "hard clusters maximize mutual information"
    - "on the entropy of couplings"
    - "quantization of binary input discrete memoryless channels"

Connected through hyperedge HE-4 (induced by source paper "supervised learning of labeled pointcloud differences via cover tree entropy reduction"):
  with members:
    - "fast construction of nets in low dimensional metrics and their applications"

Connected through hyperedge HE-5 (induced by source paper "a categorical characterization of relative entropy on polish spaces"):
  with members:
    - "a bayesian characterization of relative entropy"

Connected through hyperedge HE-6 (induced by source paper "a categorical characterization of relative entropy on standard borel spaces"):
  with members:
    - "a bayesian characterization of relative entropy"

Connected through hyperedge HE-7 (induced by source paper "a generalization of majorization that characterizes shannon entropy"):
  with members:
    - "renyi divergence and kullback leibler divergence"

Connected through hyperedge HE-8 (induced by source paper "information measures for deterministic input output systems"):
  with members:
    - "relative information loss in the pca"
    - "signal enhancement as minimization of relevant information loss"
    - "information loss in static nonlinearities"
    - "on the information loss in memoryless systems the multivariate case"
    - "optimal phase transitions in compressed sensing"
    - "some results on the information loss in dynamical systems"

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)