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: "an improved lptc neural model for background motion direction estimation"
  Abstract: A class of specialized neurons, called lobula plate tangential cells (LPTCs) has been shown to respond strongly to wide-field motion. The classic model, elementary motion detector (EMD) and its improved model, two-quadrant detector (TQD) have been proposed to simulate LPTCs. Although EMD and TQD can percept background motion, their outputs are so cluttered that it is difficult to discriminate actual motion direction of the background. In this paper, we propose a max operation mechanism to model a newly-found transmedullary neuron Tm9 whose physiological properties do not map onto EMD and TQD. This proposed max operation mechanism is able to improve the detection performance of TQD in cluttered background by filtering out irrelevant motion signals. We will demonstrate the functionality of this proposed mechanism in wide-field motion perception.

Connected through hyperedge HE-1 (induced by source paper "a robust visual system for small target motion detection against cluttered moving backgrounds"):
  with members:
    - "a directionally selective small target motion detecting visual neural network in cluttered backgrounds"
    - "robust online matrix factorization for dynamic background subtraction"

Connected through hyperedge HE-2 (induced by source paper "visual cue integration for small target motion detection in natural cluttered backgrounds"):
  with members:
    - "a directionally selective small target motion detecting visual neural network in cluttered backgrounds"
    - "a feedback neural network for small target motion detection in cluttered backgrounds"

Connected through hyperedge HE-3 (induced by source paper "towards computational models and applications of insect visual systems for motion perception a review"):
  with members:
    - "a bio inspired collision detecotr for small quadcopter"
    - "a feedback neural network for small target motion detection in cluttered backgrounds"

Connected through hyperedge HE-4 (induced by source paper "a directionally selective neural network with separated on and off pathways for translational motion perception in a visually cluttered environment"):
  with members:
    - "independent motion detection with event driven cameras"

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.CV(Computer Vision and Pattern Recognition)