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: "new principles of coordination in large scale micro and molecular robotic groups"
  Abstract: Abstract—Micro- and molecular-robotic systems act as large-scale swarms. Capabilities of sensing, communication and infor-mation processing are very limited on these scales. This shortposition paper describes a swarm-based minimalistic approach,which can be applied for coordinating collective behavior in suchsystems. I. I NTRODUCTION Micro-robots and currently molecular-robotics [2] becomeimportant and extremely challenging branch of modern roboticresearch. The domain of micro- and molecular-robotic appli-cations, such as biotechnologies, micro-systems construction,molecular engineering, and ﬁnally nanotechnologies, repre-sents a huge economic, social and technological potentialwith a great impact on everyday human life [1]. Due to alarge number of micro-objects, usually they are of hundredthousands, there is a need of such robotic systems, whichcan provide an autonomous and half-autonomous handlingof many small objects. The main focus lies on a high-parallelization of handling processes performed by collectivelyworking robots and involves different plan-generating [19] andplanning [16] approaches.Micro-robots and especially molecular systems, due toa small size, are very limited in actuation, sensing andcommunication [22]. However working in a collective wayrequires coordination of robotic behavior. The technological,size-dependent and functional restrictions hinder applicationsof approaches known in a larger mini- and ”normal-size”robotics. As demonstrated by a few current micro-roboticprojects (robot size 2 2 2mm) [3], we need to ﬁnd anew principles of coordination even in the micro-robotic groupconsisting of 1000 robots.An approach, which can be successfully applied to limitedrobotic systems, is based on the low-level properties of opticalperception [21], in particular IR light [14], as well as oninvolvement of such processes into e.g. collective decisionmaking [13] or cognitive processes [15]. More generally,different nonlinear processes and models [24] can be used forsuch purposes. Further in Sec. II we provide an example oftypical problems in limited micro-systems such as collectiveinformation processing and in Sec. III brieﬂy discuss a mech-anism for solution of this problem.

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.RO(Robotics)