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: "the one way communication complexity of dynamic time warping distance"
  Abstract: We resolve the randomized one-way communication complexity of Dynamic Time Warping (DTW) distance. We show that there is an efficient one-way communication protocol using $\widetilde{O}(n/\alpha)$ bits for the problem of computing an $\alpha$-approximation for DTW between strings $x$ and $y$ of length $n$, and we prove a lower bound of $\Omega(n / \alpha)$ bits for the same problem. Our communication protocol works for strings over an arbitrary metric of polynomial size and aspect ratio, and we optimize the logarithmic factors depending on properties of the underlying metric, such as when the points are low-dimensional integer vectors equipped with various metrics or have bounded doubling dimension. We also consider linear sketches of DTW, showing that such sketches must have size $\Omega(n)$.

Connected through hyperedge HE-1 (induced by source paper "dynamic time warping in strongly subquadratic time algorithms for the low distance regime and approximate evaluation"):
  with members:
    - "approximating edit distance within constant factor in truly sub quadratic time"
    - "on estimating edit distance alignment dimension reduction and embeddings"
    - "locality sensitive hashing of curves"
    - "products of euclidean metrics and applications to proximity questions among curves"
    - "streaming algorithms for computing edit distance without exploiting suffix trees"
    - "dynamic time warping and geometric edit distance breaking the quadratic barrier"
    - "quadratic conditional lower bounds for string problems and dynamic time warping"
    - "approximating dynamic time warping and edit distance for a pair of point sequences"

Connected through hyperedge HE-2 (induced by source paper "random projections and sampling algorithms for clustering of high dimensional polygonal curves"):
  with members:
    - "on the hardness of computing an average curve"
    - "walking the dog fast in practice algorithm engineering of the fr echet distance"
    - "beyond worst case analysis"
    - "on coresets for logistic regression"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "strong coresets for k median and subspace approximation goodbye dimension"
    - "a fast implementation of near neighbors queries for frechet distance gis cup"
    - "clustering is difficult only when it does not matter"

Connected through hyperedge HE-3 (induced by source paper "sublinear data structures for short fr echet queries"):
  with members:
    - "approximate nearest neighbor for curves simple efficient and deterministic"
    - "on optimal polyline simplification using the hausdorff and fr echet distance"
    - "a fast implementation of near neighbors queries for frechet distance gis cup"
    - "on the complexity of range searching among curves"
    - "products of euclidean metrics and applications to proximity questions among curves"
    - "coresets and sketches"
    - "why walking the dog takes time frechet distance has no strongly subquadratic algorithms unless seth fails"
    - "fast construction of nets in low dimensional metrics and their applications"

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)