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: "discovery of convoys in trajectory databases"
  Abstract: As mobile devices with positioning capabilities continue to proliferate, data management for so-called trajectory databases that capture the historical movements of populations of moving objects becomes important. This paper considers the querying of such databases for convoys, a convoy being a group of objects that have traveled together for some time. More specifically, this paper formalizes the concept of a convoy query using density-based notions, in order to capture groups of arbitrary extents and shapes. Convoy discovery is relevant for real-life applications in throughput planning of trucks and carpooling of vehicles. Although there has been extensive research on trajectories in the literature, none of this can be applied to retrieve correctly exact convoy result sets. Motivated by this, we develop three efficient algorithms for convoy discovery that adopt the well-known filter-refinement framework. In the filter step, we apply line-simplification techniques on the trajectories and establish distance bounds between the simplified trajectories. This permits efficient convoy discovery over the simplified trajectories without missing any actual convoys. In the refinement step, the candidate convoys are further processed to obtain the actual convoys. Our comprehensive empirical study offers insight into the properties of the paper's proposals and demonstrates that the proposals are effective and efficient on real-world trajectory data.

Connected through hyperedge HE-1 (induced by source paper "finding route hotspots in large labeled networks"):
  with members:
    - "finding theme communities from database networks"
    - "when engagement meets similarity efficient k r core computation on social networks"
    - "truss decomposition in massive networks"
    - "efficient discovery of association rules and frequent itemsets through sampling with tight performance guarantees"

Connected through hyperedge HE-2 (induced by source paper "scalable distributed subtrajectory clustering"):
  with members:
    - "distributed subtrajectory join on massive datasets"

Connected through hyperedge HE-3 (induced by source paper "a survey on trajectory clustering analysis"):
  with members:
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "globally continuous and non markovian crowd activity analysis from videos"
    - "a robust and efficient video representation for action recognition"
    - "learning to segment moving objects in videos"
    - "a gesture recognition system for detecting behavioral patterns of adhd"
    - "classifying and visualizing motion capture sequences using deep neural networks"
    - "integrating graph partitioning and matching for trajectory analysis in video surveillance"
    - "ganc greedy agglomerative normalized cut"

Connected through hyperedge HE-4 (induced by source paper "group evolution patterns in running races"):
  with members:
    - "minimum weight connectivity augmentation for planar straight line graphs"

Connected through hyperedge HE-5 (induced by source paper "spatio temporal data mining a survey of problems and methods"):
  with members:
    - "classification of passes in football matches using spatiotemporal data"
    - "theory guided data science a new paradigm for scientific discovery from data"
    - "latent space model for road networks to predict time varying traffic"
    - "structural rnn deep learning on spatio temporal graphs"
    - "on the spatiotemporal burstiness of terms"
    - "empirical comparison of algorithms for network community detection"

Connected through hyperedge HE-6 (induced by source paper "netclus a scalable framework for locating top k sites for placement of trajectory aware services"):
  with members:
    - "trajectory aware macro cell planning for mobile users"
    - "inferring uncertain trajectories from partial observations"

Connected through hyperedge HE-7 (induced by source paper "inferring unusual crowd events from mobile phone call detail records"):
  with members:
    - "the call of the crowd event participation in location based social services"
    - "mining users significant driving routes with low power sensors"

Connected through hyperedge HE-8 (induced by source paper "towards distributed convoy pattern mining"):
  with members:
    - "efficient task replication for fast response times in parallel computation"

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)