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: "sparsedtw a novel approach to speed up dynamic time warping"
  Abstract: We present a new space-efficient approach, (SparseDTW), to compute the Dynamic Time Warping (DTW) distance between two time series that always yields the optimal result. This is in contrast to other known approaches which typically sacrifice optimality to attain space efficiency. The main idea behind our approach is to dynamically exploit the existence of similarity and/or correlation between the time series. The more the similarity between the time series the less space required to compute the DTW between them. To the best of our knowledge, all other techniques to speedup DTW, impose apriori constraints and do not exploit similarity characteristics that may be present in the data. We conduct experiments and demonstrate that SparseDTW outperforms previous approaches.

Connected through hyperedge HE-1 (induced by source paper "setting the yardstick a quantitative metric for effectively measuring tactile internet"):
  with members:
    - "ultrareliable and low latency communication techniques for tactile internet services"
    - "federated learning for ultra reliable low latency v2v communications"
    - "congestion control for network aware telehaptic communication"

Connected through hyperedge HE-2 (induced by source paper "the one way communication complexity of dynamic time warping distance"):
  with members:
    - "locality sensitive hashing of curves"
    - "products of euclidean metrics and applications to proximity questions among curves"
    - "edit distance sketching streaming and document exchange"
    - "dynamic time warping and geometric edit distance breaking the quadratic barrier"
    - "efficient deterministic single round document exchange for edit distance"
    - "quadratic conditional lower bounds for string problems and dynamic time warping"
    - "approximating dynamic time warping and edit distance for a pair of point sequences"
    - "voice recognition algorithms using mel frequency cepstral coefficient mfcc and dynamic time warping dtw techniques"

Connected through hyperedge HE-3 (induced by source paper "sparsification of the alignment path search space in dynamic time warping"):
  with members:
    - "times series averaging and denoising from a probabilistic perspective on time elastic kernels"
    - "on recursive edit distance kernels with application to time series classification"
    - "an empirical evaluation of similarity measures for time series classification"
    - "time warp edit distance with stiffness adjustment for time series matching"
    - "a kernel for time series based on global alignments"

Connected through hyperedge HE-4 (induced by source paper "efficient metric learning for the analysis of motion data"):
  with members:
    - "modeep a deep learning framework using motion features for human pose estimation"
    - "a survey on metric learning for feature vectors and structured data"
    - "markerless motion capture in the crowd"

Connected through hyperedge HE-5 (induced by source paper "twofold video hashing with automatic synchronization"):
  with members:
    - "hashing for similarity search a survey"

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)