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: "inner scene similarities as a contextual cue for object detection"
  Abstract: Using image context is an effective approach for improving object detection. Previously proposed methods used contextual cues that rely on semantic or spatial information. In this work, we explore a different kind of contextual information: inner-scene similarity. We present the CISS (Context by Inner Scene Similarity) algorithm, which is based on the observation that two visually similar sub-image patches are likely to share semantic identities, especially when both appear in the same image. CISS uses base-scores provided by a base detector and performs as a post-detection stage. For each candidate sub-image (denoted anchor), the CISS algorithm finds a few similar sub-images (denoted supporters), and, using them, calculates a new enhanced score for the anchor. This is done by utilizing the base-scores of the supporters and a pre-trained dependency model. The new scores are modeled as a linear function of the base scores of the anchor and the supporters and is estimated using a minimum mean square error optimization. This approach results in: (a) improved detection of partly occluded objects (when there are similar non-occluded objects in the scene), and (b) fewer false alarms (when the base detector mistakenly classifies a background patch as an object). This work relates to Duncan and Humphreys' "similarity theory," a psychophysical study. which suggested that the human visual system perceptually groups similar image regions and that the classification of one region is affected by the estimated identity of the other. Experimental results demonstrate the enhancement of a base detector's scores on the PASCAL VOC dataset.

Connected through hyperedge HE-1 (induced by source paper "seeing without looking contextual rescoring of object detections for ap maximization"):
  with members:
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "structure inference net object detection using scene level context and instance level relationships"
    - "attention is all you need"
    - "cascade r cnn delving into high quality object detection"
    - "learning non maximum suppression"
    - "spatial memory for context reasoning in object detection"
    - "exploring the bounds of the utility of context for object detection"
    - "accurate single stage detector using recurrent rolling convolution"

Connected through hyperedge HE-2 (induced by source paper "contextual object detection with a few relevant neighbors"):
  with members:
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "recovering hard to find object instances by sampling context based object proposals"
    - "the role of context selection in object detection"
    - "attentive contexts for object detection"
    - "inside outside net detecting objects in context with skip pooling and recurrent neural networks"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "microsoft coco common objects in context"

Connected through hyperedge HE-3 (induced by source paper "exploring the bounds of the utility of context for object detection"):
  with members:
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "deep feature based contextual model for object detection"
    - "ssd single shot multibox detector"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "the role of context selection in object detection"
    - "attentive contexts for object detection"
    - "inside outside net detecting objects in context with skip pooling and recurrent neural networks"
    - "faster r cnn towards real time object detection with region proposal networks"

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)