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: "towards flexible teamwork"
  Abstract: Many AI researchers are today striving to build agent teams for complex, dynamic multi-agent domains, with intended applications in arenas such as education, training, entertainment, information integration, and collective robotics. Unfortunately, uncertainties in these complex, dynamic domains obstruct coherent teamwork. In particular, team members often encounter differing, incomplete, and possibly inconsistent views of their environment. Furthermore, team members can unexpectedly fail in fulfilling responsibilities or discover unexpected opportunities. Highly flexible coordination and communication is key in addressing such uncertainties. Simply fitting individual agents with precomputed coordination plans will not do, for their inflexibility can cause severe failures in teamwork, and their domain-specificity hinders reusability. Our central hypothesis is that the key to such flexibility and reusability is providing agents with general models of teamwork. Agents exploit such models to autonomously reason about coordination and communication, providing requisite flexibility. Furthermore, the models enable reuse across domains, both saving implementation effort and enforcing consistency. This article presents one general, implemented model of teamwork, called STEAM. The basic building block of teamwork in STEAM is joint intentions (Cohen & Levesque, 1991b); teamwork in STEAM is based on agents' building up a (partial) hierarchy of joint intentions (this hierarchy is seen to parallel Grosz & Kraus's partial SharedPlans, 1996). Furthermore, in STEAM, team members monitor the team's and individual members' performance, reorganizing the team as necessary. Finally, decision-theoretic communication selectivity in STEAM ensures reduction in communication overheads of teamwork, with appropriate sensitivity to the environmental conditions. This article describes STEAM's application in three different complex domains, and presents detailed empirical results.

Connected through hyperedge HE-1 (induced by source paper "autonomous agents modelling other agents a comprehensive survey and open problems"):
  with members:
    - "reasoning about hypothetical agent behaviours and their parameters"
    - "are you doing what i think you are doing criticising uncertain agent models"
    - "e hba using action policies for expert advice and agent typification"
    - "on convergence and optimality of best response learning with policy types in multiagent systems"
    - "an empirical study on the practical impact of prior beliefs over policy types"
    - "heuristic online goal recognition in continuous domains"
    - "a survey of learning in multiagent environments dealing with non stationarity"
    - "belief and truth in hypothesised behaviours"

Connected through hyperedge HE-2 (induced by source paper "cooperative multi agent planning a survey"):
  with members:
    - "fmap distributed cooperative multi agent planning"
    - "an approach to multi agent planning with incomplete information"
    - "a flexible coupling approach to multi agent planning under incomplete information"
    - "game theoretic approach for non cooperative planning"
    - "a formal analysis of required cooperation in multi agent planning"
    - "the lama planner guiding cost based anytime planning with landmarks"
    - "the fast downward planning system"
    - "ordered landmarks in planning"

Connected through hyperedge HE-3 (induced by source paper "belief and truth in hypothesised behaviours"):
  with members:
    - "on convergence and optimality of best response learning with policy types in multiagent systems"
    - "an empirical study on the practical impact of prior beliefs over policy types"
    - "a game theoretic model and best response learning method for ad hoc coordination in multiagent systems"
    - "model based bayesian exploration"
    - "the complexity of decentralized control of markov decision processes"
    - "bayes bluff opponent modelling in poker"
    - "a framework for sequential planning in multi agent settings"
    - "awesome a general multiagent learning algorithm that converges in self play and learns a best response against stationary opponents"

Connected through hyperedge HE-4 (induced by source paper "a flexible coupling approach to multi agent planning under incomplete information"):
  with members:
    - "the lama planner guiding cost based anytime planning with landmarks"
    - "the fast downward planning system"
    - "a framework for sequential planning in multi agent settings"
    - "vhpop versatile heuristic partial order planner"
    - "monitoring teams by overhearing a multi agent plan recognition approach"
    - "the ff planning system fast plan generation through heuristic search"
    - "partial order planning with concurrent interacting actions"

Connected through hyperedge HE-5 (induced by source paper "an approach to multi agent planning with incomplete information"):
  with members:
    - "vhpop versatile heuristic partial order planner"
    - "the ff planning system fast plan generation through heuristic search"
    - "partial order planning with concurrent interacting actions"

Connected through hyperedge HE-6 (induced by source paper "on the formal semantics of speech act based communication in an agent oriented programming language"):
  with members:
    - "knowledge and common knowledge in a distributed environment"

Connected through hyperedge HE-7 (induced by source paper "hybrid bdi pomdp framework for multiagent teaming"):
  with members:
    - "learning to cooperate via policy search"
    - "incremental pruning a simple fast exact method for partially observable markov decision processes"
    - "the complexity of decentralized control of markov decision processes"
    - "the communicative multiagent team decision problem analyzing teamwork theories and models"
    - "towards adjustable autonomy for the real world"

Connected through hyperedge HE-8 (induced by source paper "cooperative information sharing to improve distributed learning in multi agent systems"):
  with members:
    - "reinforcement learning for adaptive routing"
    - "decentralized supply chain formation a market protocol and competitive equilibrium analysis"
    - "antnet distributed stigmergetic control for communications networks"

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)