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 and inferring relations in cortical networks"
  Abstract: A pressing scientific challenge is to understand how brains work. Of particular interest is the neocortex,the part of the brain that is especially large in humans, capable of handling a wide variety of tasks including visual, auditory, language, motor, and abstract processing. These functionalities are processed in different self-organized regions of the neocortical sheet, and yet the anatomical structure carrying out the processing is relatively uniform across the sheet. We are at a loss to explain, simulate, or understand such a multi-functional homogeneous sheet-like computational structure - we do not have computational models which work in this way. Here we present an important step towards developing such models: we show how uniform modules of excitatory and inhibitory neurons can be connected bidirectionally in a network that, when exposed to input in the form of population codes, learns the input encodings as well as the relationships between the inputs. STDP learning rules lead the modules to self-organize into a relational network, which is able to infer missing inputs,restore noisy signals, decide between conflicting inputs, and combine cues to improve estimates. These networks show that it is possible for a homogeneous network of spiking units to self-organize so as to provide meaningful processing of its inputs. If such networks can be scaled up, they could provide an initial computational model relevant to the large scale anatomy of the neocortex.

Connected through hyperedge HE-1 (induced by source paper "a spiking network for inference of relations trained with neuromorphic backpropagation"):
  with members:
    - "a 0 086 mm 2 12 7 pj sop 64k synapse 256 neuron online learning digital spiking neuromorphic processor in 28 nm cmos"
    - "spatio temporal backpropagation for training high performance spiking neural networks"
    - "algorithm and hardware design of discrete time spiking neural networks based on back propagation with binary activations"
    - "a wake sleep algorithm for recurrent spiking neural networks"
    - "a scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors dynaps"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "training deep spiking neural networks using backpropagation"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-2 (induced by source paper "adaptive motor control and learning in a spiking neural network realised on a mixed signal neuromorphic processor"):
  with members:
    - "the event camera dataset and simulator event based data for pose estimation visual odometry and slam"
    - "a scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors dynaps"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "aggressive quadrotor flight through narrow gaps with onboard sensing and computing using active vision"
    - "precise deep neural network computation on imprecise low power analog hardware"
    - "kickstarting high performance energy efficient manycore architectures with epiphany"

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)