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: "census signal temporal logic inference for multiagent group behavior analysis"
  Abstract: In this paper, we define a novel census signal temporal logic (CensusSTL) that focuses on the number of agents in different subsets of a group that complete a certain task specified by the STL. CensusSTL consists of an “ inner logic ” STL formula and an “ outer logic ” STL formula. We present a new inference algorithm to infer CensusSTL formulas from the trajectory data of a group of agents. We first identify the “ inner logic ” STL formula and then infer the subgroups based on whether the agents’ behaviors satisfy the “ inner logic ” formula at each time point. We use two different approaches to infer the subgroups based on similarity and complementarity, respectively. The “ outer logic ” CensusSTL formula is inferred from the census trajectories of different subgroups. We apply the algorithm in analyzing data from a soccer match by inferring the CensusSTL formula for different subgroups of a soccer team.   Note to Practitioners —The method described in this paper can be used to discover subgroups of agents and their behavior patterns based on spatial–temporal data of all agents in the group. The subgroups are formed by agents that behave similarly or complementarily for a certain task. The behavior patterns of the subgroups are expressed in the form of signal temporal logic statements. These are logical statements about the number of agents in the subgroups that are in certain states during a certain time interval. For example, if there are more than two blue agents in the region, then within 2 h, there will be more than three red agents in the same region. The behavior patterns inferred using this method can be used in: 1) analysis of group behavior while performing a task, e.g., in sports tactical analysis and 2) prediction of future group behavior under similar situations.

Connected through hyperedge HE-1 (induced by source paper "adaptive teaching of temporal logic formulas to learners with preferences"):
  with members:
    - "transfer of temporal logic formulas in reinforcement learning"
    - "understanding the role of adaptivity in machine teaching the case of version space learners"
    - "information guided temporal logic inference with prior knowledge"
    - "mining parametric temporal logic properties in model based design for cyber physical systems"
    - "teaching categories to human learners with visual explanations"
    - "learning linear temporal properties"
    - "machine teaching for inverse reinforcement learning algorithms and applications"
    - "learning task specifications from demonstrations"

Connected through hyperedge HE-2 (induced by source paper "transfer of temporal logic formulas in reinforcement learning"):
  with members:
    - "information guided temporal logic inference with prior knowledge"
    - "mining parametric temporal logic properties in model based design for cyber physical systems"
    - "learning linear temporal properties"
    - "learning task specifications from demonstrations"
    - "safe reinforcement learning via shielding"
    - "q learning for robust satisfaction of signal temporal logic specifications"
    - "reinforcement learning with temporal logic rewards"
    - "probably approximately correct mdp learning and control with temporal logic constraints"

Connected through hyperedge HE-3 (induced by source paper "graph temporal logic inference for classification and identification"):
  with members:
    - "information guided temporal logic inference with prior knowledge"
    - "mining parametric temporal logic properties in model based design for cyber physical systems"
    - "learning linear temporal properties"
    - "learning task specifications from demonstrations"
    - "the robotarium a remotely accessible swarm robotics research testbed"

Connected through hyperedge HE-4 (induced by source paper "multirobot coordination with counting temporal logics"):
  with members:
    - "control of magnetic microrobot teams for temporal micromanipulation tasks"
    - "control synthesis for permutation symmetric high dimensional systems with counting constraints"
    - "the robotarium a remotely accessible swarm robotics research testbed"
    - "robust multi robot optimal path planning with temporal logic constraints"
    - "linear encodings of bounded ltl model checking"

Connected through hyperedge HE-5 (induced by source paper "information guided temporal logic inference with prior knowledge"):
  with members:
    - "learning linear temporal properties"

Connected through hyperedge HE-6 (induced by source paper "a robust genetic algorithm for learning temporal specifications from data"):
  with members:
    - "mining parametric temporal logic properties in model based design for cyber physical systems"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"

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.AI(Artificial Intelligence)