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 family of statistical symmetric divergences based on jensen s inequality"
  Abstract: We introduce a novel parametric family of symmetric information-theoretic distances based on Jensen’s inequality for a convex functional generator. In particular, this family unies the celebrated Jereys divergence with the Jensen-Shannon divergence when the Shannon entropy generator is chosen. We then design a generic algorithm to compute the unique centroid dened as the minimum average divergence. This yields a smooth family of centroids linking the Jereys to the Jensen-Shannon centroid. Finally, we report on our experimental results.

Connected through hyperedge HE-1 (induced by source paper "explain graph neural networks to understand weighted graph features in node classification"):
  with members:
    - "gnn explainer a tool for post hoc explanation of graph neural networks"
    - "explainability techniques for graph convolutional networks"
    - "graph neural networks a review of methods and applications"
    - "exploiting edge features in graph neural networks"
    - "deep learning on graphs a survey"
    - "relational inductive biases deep learning and graph networks"
    - "semi supervised classification with graph convolutional networks"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

Connected through hyperedge HE-2 (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"
    - "on the chi square and higher order chi distances for approximating f divergences"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "on a generalization of the jensen shannon divergence"):
  with members:
    - "on the jensen shannon symmetrization of distances relying on abstract means"
    - "on f divergences integral representations local behavior and inequalities"
    - "monte carlo information geometry the dually flat case"
    - "information measures the curious case of the binary alphabet"
    - "jeffreys centroids a closed form expression for positive histograms and a guaranteed tight approximation for frequency histograms"
    - "the burbea rao and bhattacharyya centroids"
    - "measures of distributional similarity"

Connected through hyperedge HE-4 (induced by source paper "topic modeling the reading and writing behavior of information foragers"):
  with members:
    - "exploration and exploitation of victorian science in darwin s reading notebooks"
    - "multi level computational methods for interdisciplinary research in the hathitrust digital library"
    - "towards evaluation of cultural scale claims in light of topic model sampling effects"
    - "the wisdom of the few supertaggers in collaborative tagging systems"
    - "accuracy of simple initials based methods for author name disambiguation"
    - "multilingual topic models for unaligned text"

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"
    - "on the chi square and higher order chi distances for approximating f divergences"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "monte carlo information geometry the dually flat case"):
  with members:
    - "on w mixtures finite convex combinations of prescribed component distributions"
    - "fast exact k means k medians and bregman divergence clustering in 1d"
    - "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"
    - "the burbea rao and bhattacharyya centroids"
    - "statistical exponential families a digest with flash cards"

Connected through hyperedge HE-7 (induced by source paper "resampling methods for particle filtering identical distribution a new method and comparable study"):
  with members:
    - "review fight sample degeneracy and impoverishment in particle filters a review of intelligent approaches"

Connected through hyperedge HE-8 (induced by source paper "bootstrap methods for the empirical study of decision making and information flows in social systems"):
  with members:
    - "robust feature selection by mutual information distributions"
    - "distribution of mutual information"

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.CV(Computer Vision and Pattern Recognition)