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 stability of persistent entropy and new summary functions for tda"
  Abstract: In this paper, we study properties of persistent entropy (the Shannon entropy of persistent barcodes) and prove its stability under small perturbations in the given input data. From this concept, we define two summary functions and show how to use them to detect topological features.

Connected through hyperedge HE-1 (induced by source paper "toporesnet a hybrid deep learning architecture and its application to skin lesion classification"):
  with members:
    - "persistence curves a canonical framework for summarizing persistence diagrams"
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "skin lesion analysis toward melanoma detection a challenge at the 2017 international symposium on biomedical imaging isbi hosted by the international skin imaging collaboration isic"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "persistence curves a canonical framework for summarizing persistence diagrams"):
  with members:
    - "from bow to cnn two decades of texture representation for texture classification"
    - "sliced wasserstein kernel for persistence diagrams"

Connected through hyperedge HE-3 (induced by source paper "persistent entropy a scale invariant topological statistic for analyzing cell arrangements"):
  with members:
    - "fast and accurate tumor segmentation of histology images using persistent homology and deep convolutional features"

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)