human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "gaussian processes with context supported priors for active object localization"):
  Abstract: We devise an algorithm using a Bayesian optimization framework in conjunction with contextual visual data for the efficient localization of objects in still images. Recent research has demonstrated substantial progress in object localization and related tasks for computer vision. However, many current state-of-the-art object localization procedures still suffer from inaccuracy and inefficiency, in addition to failing to provide a principled and interpretable system amenable to high-level vision tasks. We address these issues with the current research. #R##N#Our method encompasses an active search procedure that uses contextual data to generate initial bounding-box proposals for a target object. We train a convolutional neural network to approximate an offset distance from the target object. Next, we use a Gaussian Process to model this offset response signal over the search space of the target. We then employ a Bayesian active search for accurate localization of the target. #R##N#In experiments, we compare our approach to a state-of-theart bounding-box regression method for a challenging pedestrian localization task. Our method exhibits a substantial improvement over this baseline regression method.

Includes members:
  Member "relief r cnn utilizing convolutional features for fast object detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards symbolic reinforcement learning with common sense")
  Member "active object localization in visual situations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "context based object viewpoint estimation a 2d relational approach")
  Member "how far are we from solving pedestrian detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pedestrian detection the elephant in the room")
  Member "deep feature based contextual model for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "object detection with convolutional neural networks")
  Member "recovering hard to find object instances by sampling context based object proposals":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "contextual object detection with a few relevant neighbors")
  Member "fast on line kernel density estimation for active object localization":
    also connected through other hyperedges:
      (none)
  Member "searching for objects using structure in indoor scenes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "active object localization in visual situations")
  Member "improving object detection with deep convolutional networks via bayesian optimization and structured prediction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adaptively denoising proposal collection for weakly supervised object localization")

Question: Please tell me which class the source paper 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)