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: "tracking live fish from low contrast and low frame rate stereo videos"
  Abstract: Nonextractive fish abundance estimation with the aid of visual analysis has drawn increasing attention. Unstable illumination, ubiquitous noise, and low-frame-rate (LFR) video capturing in the underwater environment, however, make conventional tracking methods unreliable. In this paper, we present a multiple fish-tracking system for low-contrast and LFR stereo videos with the use of a trawl-based underwater camera system. An automatic fish segmentation algorithm overcomes the low-contrast issues by adopting a histogram backprojection approach on double local-thresholded images to ensure an accurate segmentation on the fish shape boundaries. Built upon a reliable feature-based object matching method, a multiple-target tracking algorithm via a modified Viterbi data association is proposed to overcome the poor motion continuity and frequent entrance/exit of fish targets under LFR scenarios. In addition, a computationally efficient block-matching approach performs successful stereo matching that enables an automatic fish-body tail compensation to greatly reduce segmentation error and allows for an accurate fish length measurement. Experimental results show that an effective and reliable tracking performance for multiple live fish with underwater stereo cameras is achieved.

Connected through hyperedge HE-1 (induced by source paper "do it yourself single camera 3d pointer input device"):
  with members:
    - "4 dof tracking for robot fine manipulation tasks"

Connected through hyperedge HE-2 (induced by source paper "a feature learning and object recognition framework for underwater fish images"):
  with members:
    - "unsupervised discovery of mid level discriminative patches"

Connected through hyperedge HE-3 (induced by source paper "underwater fish tracking for moving cameras based on deformable multiple kernels"):
  with members:
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

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)