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 object localization in visual situations"
  Abstract: We describe a method for performing active localization of objects in instances of visual situations. A visual situation is an abstract concept---e.g., "a boxing match", "a birthday party", "walking the dog", "waiting for a bus"---whose image instantiations are linked more by their common spatial and semantic structure than by low-level visual similarity. Our system combines given and learned knowledge of the structure of a particular situation, and adapts that knowledge to a new situation instance as it actively searches for objects. More specifically, the system learns a set of probability distributions describing spatial and other relationships among relevant objects. The system uses those distributions to iteratively sample object proposals on a test image, but also continually uses information from those object proposals to adaptively modify the distributions based on what the system has detected. We test our approach's ability to efficiently localize objects, using a situation-specific image dataset created by our group. We compare the results with several baselines and variations on our method, and demonstrate the strong benefit of using situation knowledge and active context-driven localization. Finally, we contrast our method with several other approaches that use context as well as active search for object localization in images.

Connected through hyperedge HE-1 (induced by source paper "gaussian processes with context supported priors for active object localization"):
  with members:
    - "relief r cnn utilizing convolutional features for fast object detection"
    - "how far are we from solving pedestrian detection"
    - "deep feature based contextual model for object detection"
    - "recovering hard to find object instances by sampling context based object proposals"
    - "fast on line kernel density estimation for active object localization"
    - "searching for objects using structure in indoor scenes"
    - "improving object detection with deep convolutional networks via bayesian optimization and structured prediction"
    - "uavs using bayesian optimization to locate wifi devices"

Connected through hyperedge HE-2 (induced by source paper "context based object viewpoint estimation a 2d relational approach"):
  with members:
    - "recovering hard to find object instances by sampling context based object proposals"
    - "render for cnn viewpoint estimation in images using cnns trained with rendered 3d model views"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "viewpoints and keypoints"
    - "discriminative probabilistic models for relational data"

Connected through hyperedge HE-3 (induced by source paper "fast on line kernel density estimation for active object localization"):
  with members:
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "adaptive object detection using adjacency and zoom prediction"
    - "active object localization with deep reinforcement learning"
    - "an active search strategy for efficient object class detection"
    - "imagenet large scale visual recognition challenge"

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)