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: "abmof a novel optical flow algorithm for dynamic vision sensors"
  Abstract: Dynamic Vision Sensors (DVS), which output asynchronous log intensity change events, have potential applications in high-speed robotics, autonomous cars and drones. The precise event timing, sparse output, and wide dynamic range of the events are well suited for optical flow, but conventional optical flow (OF) algorithms are not well matched to the event stream data. This paper proposes an event-driven OF algorithm called adaptive block-matching optical flow (ABMOF). ABMOF uses time slices of accumulated DVS events. The time slices are adaptively rotated based on the input events and OF results. Compared with other methods such as gradient-based OF, ABMOF can efficiently be implemented in compact logic circuits. Results show that ABMOF achieves comparable accuracy to conventional standards such as Lucas-Kanade (LK). The main contributions of our paper are new adaptive time-slice rotation methods that ensure the generated slices have sufficient features for matching,including a feedback mechanism that controls the generated slices to have average slice displacement within the block search range. An LK method using our adapted slices is also implemented. The ABMOF accuracy is compared with this LK method on natural scene data including sparse and dense texture, high dynamic range, and fast motion exceeding 30,000 pixels per second.The paper dataset and source code are available from this http URL

Connected through hyperedge HE-1 (induced by source paper "spike flownet event based optical flow estimation with energy efficient hybrid neural networks"):
  with members:
    - "unsupervised learning of a hierarchical spiking neural network for optical flow estimation from events to global motion perception"
    - "towards scalable efficient and accurate deep spiking neural networks with backward residual connections stochastic softmax and hybridization"
    - "a unifying contrast maximization framework for event cameras with applications to motion depth and optical flow estimation"
    - "unsupervised event based learning of optical flow depth and egomotion"
    - "the multi vehicle stereo event camera dataset an event camera dataset for 3d perception"
    - "ev flownet self supervised optical flow estimation for event based cameras"
    - "spiking optical flow for event based sensors using ibm s truenorth neurosynaptic system"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"

Connected through hyperedge HE-2 (induced by source paper "unsupervised learning of a hierarchical spiking neural network for optical flow estimation from events to global motion perception"):
  with members:
    - "a unifying contrast maximization framework for event cameras with applications to motion depth and optical flow estimation"
    - "event based moving object detection and tracking"
    - "unsupervised learning of dense optical flow and depth from sparse event data"
    - "vertical landing for micro air vehicles using event based optical flow"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "spatio temporal backpropagation for training high performance spiking neural networks"
    - "ev flownet self supervised optical flow estimation for event based cameras"
    - "slayer spike layer error reassignment in time"

Connected through hyperedge HE-3 (induced by source paper "ev flownet self supervised optical flow estimation for event based cameras"):
  with members:
    - "the event camera dataset and simulator event based data for pose estimation visual odometry and slam"
    - "guided optical flow learning"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"
    - "stacked hourglass networks for human pose estimation"
    - "steering a predator robot using a mixed frame event driven convolutional neural network"
    - "back to basics unsupervised learning of optical flow via brightness constancy and motion smoothness"
    - "u net convolutional networks for biomedical image segmentation"
    - "bioinspired visual motion estimation"

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)