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: "press a novel framework of trajectory compression in road networks"
  Abstract: Location data becomes more and more important. In this paper, we focus on the trajectory data, and propose a new framework, namely PRESS (Paralleled Road-Network-Based Trajectory Compression), to effectively compress trajectory data under road network constraints. Different from existing work, PRESS proposes a novel representation for trajectories to separate the spatial representation of a trajectory from the temporal representation, and proposes a Hybrid Spatial Compression (HSC) algorithm and error Bounded Temporal Compression (BTC) algorithm to compress the spatial and temporal information of trajectories respectively. PRESS also supports common spatial-temporal queries without fully decompressing the data. Through an extensive experimental study on real trajectory dataset, PRESS significantly outperforms existing approaches in terms of saving storage cost of trajectory data with bounded errors.

Connected through hyperedge HE-1 (induced by source paper "gract a grammar based compressed index for trajectory data"):
  with members:
    - "one pass error bounded trajectory simplification"
    - "gract a grammar based compressed representation of trajectories"
    - "queries on lz bounded encodings"
    - "from theory to practice plug and play with succinct data structures"
    - "succinct representations of permutations and functions"

Connected through hyperedge HE-2 (induced by source paper "one pass trajectory simplification using the synchronous euclidean distance"):
  with members:
    - "one pass error bounded trajectory simplification"
    - "a novel framework for online amnesic trajectory compression in resource constrained environments"
    - "bounded quadrant system error bounded trajectory compression on the go"

Connected through hyperedge HE-3 (induced by source paper "cinct compression and retrieval for massive vehicular trajectories via relative movement labeling"):
  with members:
    - "csa fast pattern search for large alphabets"
    - "compact trip representation over networks"
    - "on compressing permutations and adaptive sorting"
    - "fixed block compression boosting in fm indexes"

Connected through hyperedge HE-4 (induced by source paper "one pass error bounded trajectory simplification"):
  with members:
    - "bounded quadrant system error bounded trajectory compression on the go"
    - "minimum link paths revisited"

Connected through hyperedge HE-5 (induced by source paper "prediction based online trajectory compression"):
  with members:
    - "inferring uncertain trajectories from partial observations"
    - "on prediction using variable order markov models"

Connected through hyperedge HE-6 (induced by source paper "a novel framework for online amnesic trajectory compression in resource constrained environments"):
  with members:
    - "bounded quadrant system error bounded trajectory compression on the go"

Connected through hyperedge HE-7 (induced by source paper "a simple baseline for travel time estimation using large scale trip data"):
  with members:
    - "travel time estimation for urban road networks using low frequency probe vehicle data"
    - "on the complexity of nonnegative matrix factorization"

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)