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: "one pass error bounded trajectory simplification"
  Abstract: Nowadays, various sensors are collecting, storing and transmitting tremendous trajectory data, and it is known that raw trajectory data seriously wastes the storage, network band and computing resource. Line simplification (LS) algorithms are an effective approach to attacking this issue by compressing data points in a trajectory to a set of continuous line segments, and are commonly used in practice. However, existing LS algorithms are not sufficient for the needs of sensors in mobile devices. In this study, we first develop a one-pass error bounded trajectory simplification algorithm (OPERB), which scans each data point in a trajectory once and only once. We then propose an aggressive one-pass error bounded trajectory simplification algorithm (OPERB-A), which allows interpolating new data points into a trajectory under certain conditions. Finally, we experimentally verify that our approaches (OPERB and OPERB-A) are both efficient and effective, using four real-life trajectory datasets.

Connected through hyperedge HE-1 (induced by source paper "approximate computation for big data analytics"):
  with members:
    - "graph pattern matching for dynamic team formation"
    - "investigative simulation towards utilizing graph pattern matching for investigative search"
    - "graph based anomaly detection and description a survey"
    - "bounded quadrant system error bounded trajectory compression on the go"
    - "shortest path and distance queries on road networks towards bridging theory and practice"
    - "capturing topology in graph pattern matching"

Connected through hyperedge HE-2 (induced by source paper "gract a grammar based compressed index for trajectory data"):
  with members:
    - "gract a grammar based compressed representation of trajectories"
    - "queries on lz bounded encodings"
    - "press a novel framework of trajectory compression in road networks"
    - "from theory to practice plug and play with succinct data structures"
    - "succinct representations of permutations and functions"

Connected through hyperedge HE-3 (induced by source paper "one pass trajectory simplification using the synchronous euclidean distance"):
  with members:
    - "a novel framework for online amnesic trajectory compression in resource constrained environments"
    - "press a novel framework of trajectory compression in road networks"
    - "bounded quadrant system error bounded trajectory compression on the go"

Connected through hyperedge HE-4 (induced by source paper "approximate curve restricted simplification of polygonal curves"):
  with members:
    - "on optimal min curve simplification problem"
    - "data structures for halfplane proximity queries and incremental voronoi diagrams"

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)