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: "multiembedding of metric spaces"
  Abstract: Metric embedding has become a common technique in the design of algorithms. Its applicability is often dependent on how large the embedding's distortion is. For example, embedding finite metric space into trees may require linear distortion as a function of the size of the metric. Using probabilistic metric embeddings, the bound on the distortion reduces to logarithmic in the size of the metric.#R##N#We make a step in the direction of bypassing the lower bound on the distortion in terms of the size of the metric. We define "multiembeddings" of metric spaces, in which a point is mapped onto a set of points, while keeping the target metric of polynomial size and preserving the distortion of paths. The distortion obtained with such multiembeddings into ultrametrics is at most $O(\log \Delta\log\log \Delta)$, where $\Delta$ is the aspect ratio of the metric. In particular, for expander graphs, we are able to obtain constant distortion embeddings into trees, in contrast with the $\Omega(\log n)$ lower bound for all previous notions of embeddings.#R##N#We demonstrate the algorithmic application of the new embeddings for two optimization problems: group Steiner tree and metrical task systems.

Connected through hyperedge HE-1 (induced by source paper "maximum gradient embeddings and monotone clustering"):
  with members:
    - "ramsey partitions and proximity data structures"
    - "fast construction of nets in low dimensional metrics and their applications"
    - "measured descent a new embedding method for finite metrics"
    - "lower stretch spanning trees"
    - "greedy facility location algorithms analyzed using dual fitting with factor revealing lp"

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)