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: "tree edit distance learning via adaptive symbol embeddings"
  Abstract: Metric learning has the aim to improve classification accuracy by learning a distance measure which brings data points from the same class closer together and pushes data points from different classes further apart. Recent research has demonstrated that metric learning approaches can also be applied to trees, such as molecular structures, abstract syntax trees of computer programs, or syntax trees of natural language, by learning the cost function of an edit distance, i.e. the costs of replacing, deleting, or inserting nodes in a tree. However, learning such costs directly may yield an edit distance which violates metric axioms, is challenging to interpret, and may not generalize well. In this contribution, we propose a novel metric learning approach for trees which we call embedding edit distance learning (BEDL) and which learns an edit distance indirectly by embedding the tree nodes as vectors, such that the Euclidean distance between those vectors supports class discrimination. We learn such embeddings by reducing the distance to prototypical trees from the same class and increasing the distance to prototypical trees from different classes. In our experiments, we show that BEDL improves upon the state-of-the-art in metric learning for trees on six benchmark data sets, ranging from computer science over biomedical data to a natural-language processing data set containing over 300,000 nodes.

Connected through hyperedge HE-1 (induced by source paper "metric learning for ordered labeled trees with pq grams"):
  with members:
    - "revisiting the tree edit distance and its backtracing a tutorial"
    - "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 "embeddings and representation learning for structured data"):
  with members:
    - "deep weisfeiler lehman assignment kernels via multiple kernel learning"
    - "constrained graph variational autoencoders for molecule design"
    - "hierarchical graph representation learning with differentiable pooling"
    - "contextual graph markov model a deep and generative approach to graph processing"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "tree to tree neural networks for program translation"
    - "junction tree variational autoencoder for molecular graph generation"
    - "lstm a search space odyssey"

Connected through hyperedge HE-3 (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"
    - "revisiting the tree edit distance and its backtracing a tutorial"
    - "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-4 (induced by source paper "revisiting the tree edit distance and its backtracing a tutorial"):
  with members:
    - "the continuous hint factory providing hints in vast and sparsely populated edit distance spaces"
    - "rted a robust algorithm for the tree edit distance"

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.LG(Machine Learning)