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: "learning absolute sound source localisation with limited supervisions"
  Abstract: An accurate auditory space map can be learned from auditory experience, for example during development or in response to altered auditory cues such as a modified pinna. We studied neural network models that learn to localise a single sound source in the horizontal plane using binaural cues based on limited supervisions. These supervisions can be unreliable or sparse in real life. First, a simple model that has unreliable estimation of the sound source location is built, in order to simulate the unreliable auditory orienting response of newborns. It is used as a Teacher that acts as a source of unreliable supervisions. Then we show that it is possible to learn a continuous auditory space map based only on noisy left or right feedbacks from the Teacher. Furthermore, reinforcement rewards from the environment are used as a source of sparse supervision. By combining the unreliable innate response and the sparse reinforcement rewards, an accurate auditory space map, which is hard to be achieved by either one of these two kind of supervisions, can eventually be learned. Our results show that the auditory space mapping can be calibrated even without explicit supervision. Moreover, this study implies a possibly more general neural mechanism where multiple sub-modules can be coordinated to facilitate each other's learning process under limited supervisions.

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.NE(Neural and Evolutionary Computing)