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: "sdtw computing dtw distances using locally relevant constraints based on salient feature alignments"
  Abstract: Many applications generate and consume temporal data and retrieval of time series is a key processing step in many application domains. Dynamic time warping (DTW) distance between time series of size N and M is computed relying on a dynamic programming approach which creates and fills an NxM grid to search for an optimal warp path. Since this can be costly, various heuristics have been proposed to cut away the potentially unproductive portions of the DTW grid. In this paper, we argue that time series often carry structural features that can be used for identifying locally relevant constraints to eliminate redundant work. Relying on this observation, we propose salient feature based sDTW algorithms which first identify robust salient features in the given time series and then find a consistent alignment of these to establish the boundaries for the warp path search. More specifically, we propose alternative fixed core&adaptive width, adaptive core&fixed width, and adaptive core&adaptive width strategies which enforce different constraints reflecting the high level structural characteristics of the series in the data set. Experiment results show that the proposed sDTW algorithms help achieve much higher accuracy in DTWcomputation and time series retrieval than fixed core & fixed width algorithms that do not leverage local features of the given time series.

Connected through hyperedge HE-1 (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"
    - "faster retrieval with a two pass dynamic time warping lower bound"

Connected through hyperedge HE-2 (induced by source paper "dense bag of temporal sift words for time series classification"):
  with members:
    - "scale invariant local descriptor for event recognition in 1d sensor signals"

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)