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 chi square and higher order chi distances for approximating f divergences"
  Abstract: We report closed-form formula for calculating the Chi square and higher-order Chi distances between statistical distributions belonging to the same exponential family with affine natural space, and instantiate those formula for the Poisson and isotropic Gaussian families. We then describe an analytic formula for the f-divergences based on Taylor expansions and relying on an extended class of Chi-type distances.

Connected through hyperedge HE-1 (induced by source paper "on the jensen shannon symmetrization of distances relying on abstract means"):
  with members:
    - "on power chi expansions of f divergences"
    - "an elementary introduction to information geometry"
    - "monte carlo information geometry the dually flat case"
    - "guaranteed bounds on information theoretic measures of univariate mixtures using piecewise log sum exp inequalities"
    - "optimal interval clustering application to bregman clustering and statistical mixture learning"
    - "generalized bhattacharyya and chernoff upper bounds on bayes error using quasi arithmetic means"
    - "bootstrap methods for the empirical study of decision making and information flows in social systems"
    - "jeffreys centroids a closed form expression for positive histograms and a guaranteed tight approximation for frequency histograms"

Connected through hyperedge HE-2 (induced by source paper "histonet predicting size histograms of object instances"):
  with members:
    - "counting the uncountable deep semantic density estimation from space"
    - "count ception counting by fully convolutional redundant counting"
    - "mask r cnn"
    - "deep watershed transform for instance segmentation"
    - "pyramid scene parsing network"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-3 (induced by source paper "on power chi expansions of f divergences"):
  with members:
    - "guaranteed bounds on information theoretic measures of univariate mixtures using piecewise log sum exp inequalities"
    - "statistical exponential families a digest with flash cards"

Connected through hyperedge HE-4 (induced by source paper "algorithmic framework for model based deep reinforcement learning with theoretical guarantees"):
  with members:
    - "universal planning networks"
    - "temporal difference models model free deep rl for model based control"
    - "dual policy iteration"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "model ensemble trust region policy optimization"
    - "zero shot visual imitation"
    - "graph networks as learnable physics engines for inference and control"
    - "sample efficient reinforcement learning with stochastic ensemble value expansion"

Connected through hyperedge HE-5 (induced by source paper "on the chain rule optimal transport distance"):
  with members:
    - "guaranteed deterministic bounds on the total variation distance between univariate mixtures"
    - "monte carlo information geometry the dually flat case"
    - "on w mixtures finite convex combinations of prescribed component distributions"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "guaranteed bounds on information theoretic measures of univariate mixtures using piecewise log sum exp inequalities"
    - "generalized bhattacharyya and chernoff upper bounds on bayes error using quasi arithmetic means"
    - "a closed form expression for the sharma mittal entropy of exponential families"
    - "on renyi and tsallis entropies and divergences for exponential families"

Connected through hyperedge HE-6 (induced by source paper "an elementary introduction to information geometry"):
  with members:
    - "on holder projective divergences"
    - "guaranteed bounds on information theoretic measures of univariate mixtures using piecewise log sum exp inequalities"
    - "on conformal divergences and their population minimizers"
    - "cramer rao lower bound and information geometry"
    - "the burbea rao and bhattacharyya centroids"
    - "statistical exponential families a digest with flash cards"
    - "hyperbolic voronoi diagrams made easy"

Connected through hyperedge HE-7 (induced by source paper "multinomial adversarial networks for multi domain text classification"):
  with members:
    - "multiple source domain adaptation with adversarial training of neural networks"
    - "least squares generative adversarial networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-8 (induced by source paper "guaranteed deterministic bounds on the total variation distance between univariate mixtures"):
  with members:
    - "guaranteed bounds on information theoretic measures of univariate mixtures using piecewise log sum exp inequalities"
    - "f divergence inequalities"
    - "on reverse pinsker inequalities"
    - "generalized bhattacharyya and chernoff upper bounds on bayes error using quasi arithmetic means"
    - "generalised pinsker inequalities"
    - "statistical exponential families a digest with flash cards"

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)