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: "multi robot adversarial patrolling facing a full knowledge opponent"
  Abstract: The problem of adversarial multi-robot patrol has gained interest in recent years, mainly due to its immediate relevance to various security applications. In this problem, robots are required to repeatedly visit a target area in a way that maximizes their chances of detecting an adversary trying to penetrate through the patrol path. When facing a strong adversary that knows the patrol strategy of the robots, if the robots use a deterministic patrol algorithm, then in many cases it is easy for the adversary to penetrate undetected (in fact, in some of those cases the adversary can guarantee penetration). Therefore this paper presents a non-deterministic patrol framework for the robots. Assuming that the strong adversary will take advantage of its knowledge and try to penetrate through the patrol's weakest spot, hence an optimal algorithm is one that maximizes the chances of detection in that point. We therefore present a polynomial-time algorithm for determining an optimal patrol under the Markovian strategy assumption for the robots, such that the probability of detecting the adversary in the patrol's weakest spot is maximized. We build upon this framework and describe an optimal patrol strategy for several robotic models based on their movement abilities (directed or undirected) and sensing abilities (perfect or imperfect), and in different environment models - either patrol around a perimeter (closed polygon) or an open fence (open polyline).

Connected through hyperedge HE-1 (induced by source paper "3d multi robot patrolling with a two level coordination strategy"):
  with members:
    - "rcamp a resilient communication aware motion planner for mobile robots with autonomous repair of wireless connectivity"
    - "segmatch segment based place recognition in 3d point clouds"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "incremental sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-2 (induced by source paper "facing multiple attacks in adversarial patrolling games with alarmed targets"):
  with members:
    - "adversarial patrolling with spatially uncertain alarm signals"

Connected through hyperedge HE-3 (induced by source paper "maintaining ad hoc communication network in area protection scenarios with adversarial agents"):
  with members:
    - "mapp a scalable multi agent path planning algorithm with tractability and completeness guarantees"
    - "exploiting subgraph structure in multi robot path planning"

Connected through hyperedge HE-4 (induced by source paper "adversarial patrolling with spatially uncertain alarm signals"):
  with members:
    - "multi resource defensive strategies for patrolling games with alarm systems"
    - "stackelberg vs nash in security games an extended investigation of interchangeability equivalence and uniqueness"

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.MA(Multiagent Systems)