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: "faster retrieval with a two pass dynamic time warping lower bound"
  Abstract: The dynamic time warping (DTW) is a popular similarity measure between time series. The DTW fails to satisfy the triangle inequality and its computation requires quadratic time. Hence, to find closest neighbors quickly, we use bounding techniques. We can avoid most DTW computations with an inexpensive lower bound (LB_Keogh). We compare LB_Keogh with a tighter lower bound (LB_Improved). We find that LB_Improved-based search is faster. As an example, our approach is 2-3 times faster over random-walk and shape time series.

Connected through hyperedge HE-1 (induced by source paper "approximate nearest neighbor for curves simple efficient and deterministic"):
  with members:
    - "sublinear data structures for short fr echet queries"
    - "nonlinear dimension reduction via outer bi lipschitz extensions"
    - "optimal terminal dimensionality reduction in euclidean space"
    - "locality sensitive hashing of curves"
    - "on the complexity of range searching among curves"
    - "products of euclidean metrics and applications to proximity questions among curves"
    - "why walking the dog takes time frechet distance has no strongly subquadratic algorithms unless seth fails"

Connected through hyperedge HE-2 (induced by source paper "making the dynamic time warping distance warping invariant"):
  with members:
    - "hardness of consensus problems for circular strings and time series averaging"
    - "the ucr time series archive"
    - "nonsmooth analysis and subgradient methods for averaging in dynamic time warping spaces"
    - "exact mean computation in dynamic time warping spaces"
    - "asymmetric learning vector quantization for efficient nearest neighbor classification in dynamic time warping spaces"
    - "on the existence of a sample mean in dynamic time warping spaces"

Connected through hyperedge HE-3 (induced by source paper "proximity forest an effective and scalable distance based classifier for time series"):
  with members:
    - "times series averaging and denoising from a probabilistic perspective on time elastic kernels"
    - "fast and accurate time series classification with weasel"
    - "a time series forest for classification and feature extraction"
    - "time warp edit distance with stiffness adjustment for time series matching"

Connected through hyperedge HE-4 (induced by source paper "scalable comparison of javascript v8 bytecode traces"):
  with members:
    - "tracediff debugging unexpected code behavior using trace divergences"

Connected through hyperedge HE-5 (induced by source paper "an analytical approach to improving time warping on multidimensional time series"):
  with members:
    - "high dimensional time series generators"
    - "quadratic conditional lower bounds for string problems and dynamic time warping"
    - "why walking the dog takes time frechet distance has no strongly subquadratic algorithms unless seth fails"
    - "edit distance cannot be computed in strongly subquadratic time unless seth is false"

Connected through hyperedge HE-6 (induced by source paper "semi metrification of the dynamic time warping distance"):
  with members:
    - "nonsmooth analysis and subgradient methods for averaging in dynamic time warping spaces"
    - "on the existence of a sample mean in dynamic time warping spaces"
    - "time warp edit distance with stiffness adjustment for time series matching"

Connected through hyperedge HE-7 (induced by source paper "shapedtw shape dynamic time warping"):
  with members:
    - "the great time series classification bake off an experimental evaluation of recently proposed algorithms extended version"
    - "continuous action recognition based on sequence alignment"
    - "an experimental evaluation of nearest neighbour time series classification"
    - "invariant time series factorization"
    - "metric learning for temporal sequence alignment"
    - "a time series forest for classification and feature extraction"
    - "sdtw computing dtw distances using locally relevant constraints based on salient feature alignments"

Connected through hyperedge HE-8 (induced by source paper "an empirical evaluation of similarity measures for time series classification"):
  with members:
    - "characterization and exploitation of community structure in cover song networks"
    - "time warp edit distance with stiffness adjustment for time series matching"

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.DB(Databases)