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: "revisiting the tree edit distance and its backtracing a tutorial"
  Abstract: Almost 30 years ago, Zhang and Shasha published a seminal paper describing an efficient dynamic programming algorithm computing the tree edit distance, that is, the minimum number of node deletions, insertions, and replacements that are necessary to transform one tree into another. Since then, the tree edit distance has had widespread applications, for example in bioinformatics and intelligent tutoring systems. However, the original paper of Zhang and Shasha can be challenging to read for newcomers and it does not describe how to efficiently infer the optimal edit script. #R##N#In this contribution, we provide a comprehensive tutorial to the tree edit distance algorithm of Zhang and Shasha. We further prove metric properties of the tree edit distance, and describe efficient algorithms to infer the cheapest edit script, as well as a summary of all cheapest edit scripts between two trees.

Connected through hyperedge HE-1 (induced by source paper "metric learning for ordered labeled trees with pq grams"):
  with members:
    - "tree edit distance learning via adaptive symbol embeddings"
    - "adam a method for stochastic optimization"
    - "a survey on metric learning for feature vectors and structured data"
    - "a conditional random field for discriminatively trained finite state string edit distance"
    - "an optimal decomposition algorithm for tree edit distance"

Connected through hyperedge HE-2 (induced by source paper "adversarial edit attacks for tree data"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "adversarial attack on graph structured data"
    - "tree edit distance learning via adaptive symbol embeddings"
    - "audio adversarial examples targeted attacks on speech to text"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "hotflip white box adversarial examples for text classification"
    - "towards evaluating the robustness of neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "tree edit distance learning via adaptive symbol embeddings"):
  with members:
    - "the continuous hint factory providing hints in vast and sparsely populated edit distance spaces"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"

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.DS(Data Structures and Algorithms)