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: "active testing for face detection and localization"
  Abstract: We provide a novel search technique which uses a hierarchical model and a mutual information gain heuristic to efficiently prune the search space when localizing faces in images. We show exponential gains in computation over traditional sliding window approaches, while keeping similar performance levels.

Connected through hyperedge HE-1 (induced by source paper "action for better prediction"):
  with members:
    - "self supervised exploration via disagreement"
    - "curious ilqr resolving uncertainty in model based rl"
    - "model based reinforcement learning for atari"
    - "manipulation by feel touch based control with deep predictive models"
    - "unsupervised minimax adversarial curiosity generative adversarial networks and predictability minimization"
    - "robonet large scale multi robot learning"
    - "learning predictive models from observation and interaction"
    - "exploration by random network distillation"

Connected through hyperedge HE-2 (induced by source paper "geometry in active learning for binary and multi class image segmentation"):
  with members:
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "learning algorithms for active learning"
    - "annotating object instances with a polygon rnn"
    - "click carving segmenting objects in video with point clicks"
    - "scribblesup scribble supervised convolutional networks for semantic segmentation"
    - "becoming the expert interactive multi class machine teaching"
    - "active learning for delineation of curvilinear structures"
    - "what s the point semantic segmentation with point supervision"

Connected through hyperedge HE-3 (induced by source paper "active query driven visual search using probabilistic bisection and convolutional neural networks"):
  with members:
    - "how convolutional neural network see the world a survey of convolutional neural network visualization methods"
    - "distributed probabilistic bisection search using social learning"
    - "you only look once unified real time object detection"
    - "deep learning face attributes in the wild"
    - "collaborative 20 questions for target localization"
    - "on decentralized estimation with active queries"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "learning sampling distributions for efficient object detection"):
  with members:
    - "what makes for effective detection proposals"
    - "ten years of pedestrian detection what have we learned"
    - "how good are detection proposals really"
    - "pedestrian detection aided by deep learning semantic tasks"
    - "training effective node classifiers for cascade classification"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "learning active learning from data"):
  with members:
    - "value iteration networks"
    - "active learning for delineation of curvilinear structures"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "distributed probabilistic bisection search using social learning"):
  with members:
    - "probabilistic group testing under sum observations a parallelizable 2 approximation for entropy loss"
    - "collaborative 20 questions for target localization"
    - "on decentralized estimation with active queries"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "gossip algorithms for distributed signal processing"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"

Connected through hyperedge HE-7 (induced by source paper "robust periocular recognition by fusing sparse representations of color and geometry information"):
  with members:
    - "adaptive diffusion constrained total variation scheme with application to cartoon texture edge image decomposition"

Connected through hyperedge HE-8 (induced by source paper "asynchronous decentralized 20 questions for adaptive search"):
  with members:
    - "distributed compressed sensing for static and time varying networks"
    - "collaborative 20 questions for target localization"
    - "on decentralized estimation with active queries"
    - "a fast distributed proximal gradient method"

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)