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: "online event recognition from moving vessel trajectories"
  Abstract: We present a system for online monitoring of maritime activity over streaming positions from numerous vessels sailing at sea. The system employs an online tracking module for detecting important changes in the evolving trajectory of each vessel across time, and thus can incrementally retain concise, yet reliable summaries of its recent movement. In addition, thanks to its complex event recognition module, this system can also offer instant notification to marine authorities regarding emergency situations, such as suspicious moves in protected zones, or package picking at open sea. Not only did our extensive tests validate the performance, efficiency, and robustness of the system against scalable volumes of real-world and synthetically enlarged datasets, but its deployment against online feeds from vessels has also confirmed its capabilities for effective, real-time maritime surveillance.

Connected through hyperedge HE-1 (induced by source paper "online event recognition from moving vehicles application paper"):
  with members:
    - "semi supervised online structure learning for composite event recognition"
    - "probabilistic complex event recognition a survey"
    - "knowledge representation concepts for automated sla management"
    - "eca ruleml an approach combining eca rules with temporal interval based kr event action logics and transactional update logics"

Connected through hyperedge HE-2 (induced by source paper "composite event recognition for maritime monitoring"):
  with members:
    - "online learning of event definitions"
    - "mapping eu fishing activities using ship tracking data"
    - "incremental learning of event definitions with inductive logic programming"

Connected through hyperedge HE-3 (induced by source paper "the complex event recognition group"):
  with members:
    - "probabilistic complex event recognition a survey"
    - "event forecasting with pattern markov chains"
    - "online learning of event definitions"
    - "a probabilistic logic programming event calculus"
    - "incremental learning of event definitions with inductive logic programming"
    - "probabilistic event calculus for event recognition"
    - "on the implementation of the probabilistic logic programming language problog"

Connected through hyperedge HE-4 (induced by source paper "semi supervised online structure learning for composite event recognition"):
  with members:
    - "probabilistic complex event recognition a survey"
    - "online learning of event definitions"
    - "probabilistic event calculus for event recognition"
    - "learning from labeled and unlabeled data an empirical study across techniques and domains"

Connected through hyperedge HE-5 (induced by source paper "wayeb a tool for complex event forecasting"):
  with members:
    - "probabilistic complex event recognition a survey"
    - "event forecasting with pattern markov chains"
    - "on prediction using variable order markov models"

Connected through hyperedge HE-6 (induced by source paper "moving objects analytics survey on future location trajectory prediction methods"):
  with members:
    - "probabilistic complex event recognition a survey"
    - "online learning for ground trajectory prediction"

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.CV(Computer Vision and Pattern Recognition)