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: "metric and non metric proximity transformations at linear costs"
  Abstract: Domain specific (dis-)similarity or proximity measures used e.g. in alignment algorithms of sequence data, are popular to analyze complex data objects and to cover domain specific data properties. Without an underlying vector space these data are given as pairwise (dis-)similarities only. The few available methods for such data focus widely on similarities and do not scale to large data sets. Kernel methods are very effective for metric similarity matrices, also at large scale, but costly transformations are necessary starting with non-metric (dis-) similarities. We propose an integrative combination of Nystroem approximation, potential double centering and eigenvalue correction to obtain valid kernel matrices at linear costs in the number of samples. By the proposed approach effective kernel approaches, become accessible. Experiments with several larger (dis-)similarity data sets show that the proposed method achieves much better runtime performance than the standard strategy while keeping competitive model accuracy. The main contribution is an efficient and accurate technique, to convert (potentially non-metric) large scale dissimilarity matrices into approximated positive semi-definite kernel matrices at linear costs.

Connected through hyperedge HE-1 (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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "domain adaptation via low rank basis approximation"):
  with members:
    - "a survey on deep transfer learning"
    - "multiple subspace alignment improves domain adaptation"
    - "a unified framework for domain adaptation using metric learning on manifolds"
    - "probabilistic classifiers with low rank indefinite kernels"
    - "return of frustratingly easy domain adaptation"
    - "revisiting the nystrom method for improved large scale machine learning"

Connected through hyperedge HE-3 (induced by source paper "time series prediction for graphs in kernel and dissimilarity spaces"):
  with members:
    - "ordered decompositional dag kernels enhancements"
    - "evaluating link prediction methods"
    - "a survey on metric learning for feature vectors and structured data"
    - "time varying graphs and dynamic networks"

Connected through hyperedge HE-4 (induced by source paper "an agent based algorithm exploiting multiple local dissimilarities for clusters mining and knowledge discovery"):
  with members:
    - "fast approximation algorithms for cut based problems in undirected graphs"
    - "a local clustering algorithm for massive graphs and its application to nearly linear time graph partitioning"

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)