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: "discriminative learning of open vocabulary object retrieval and localization by negative phrase augmentation"
  Abstract: Thanks to the success of object detection technology, we can retrieve objects of the specified classes even from huge image collections. However, the current state-of-the-art object detectors (such as Faster R-CNN) can only handle pre-specified classes. In addition, large amounts of positive and negative visual samples are required for training. In this paper, we address the problem of open-vocabulary object retrieval and localization, where the target object is specified by a textual query (e.g., a word or phrase). We first propose Query-Adaptive R-CNN, a simple extension of Faster R-CNN adapted to open-vocabulary queries, by transforming the text embedding vector into an object classifier and localization regressor. Then, for discriminative training, we then propose negative phrase augmentation (NPA) to mine hard negative samples which are visually similar to the query and at the same time semantically mutually exclusive of the query. The proposed method can retrieve and localize objects specified by a textual query from one million images in only 0.5 seconds with high precision.

Connected through hyperedge HE-1 (induced by source paper "language features matter effective language representations for vision language tasks"):
  with members:
    - "bilinear attention networks"
    - "stacked cross attention for image text matching"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "deep contextualized word representations"
    - "multilevel language and vision integration for text to clip retrieval"
    - "open vocabulary phrase detection"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "packnet adding multiple tasks to a single network by iterative pruning"

Connected through hyperedge HE-2 (induced by source paper "revisiting image language networks for open ended phrase detection"):
  with members:
    - "lvis a dataset for large vocabulary instance segmentation"
    - "language features matter effective language representations for vision language tasks"
    - "neural sequential phrase grounding seqground"
    - "interpretable and globally optimal prediction for textual grounding using image concepts"
    - "stacked cross attention for image text matching"
    - "discriminative bimodal networks for visual localization and detection with natural language queries"
    - "query guided regression network with context policy for phrase grounding"
    - "learning two branch neural networks for image text matching tasks"

Connected through hyperedge HE-3 (induced by source paper "phrase localization without paired training examples"):
  with members:
    - "knowledge aided consistency for weakly supervised phrase grounding"
    - "unsupervised textual grounding linking words to image concepts"
    - "discriminative bimodal networks for visual localization and detection with natural language queries"
    - "query guided regression network with context policy for phrase grounding"
    - "conditional image text embedding networks"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "speed accuracy trade offs for modern convolutional object detectors"

Connected through hyperedge HE-4 (induced by source paper "natural vocabulary emerges from free form annotations"):
  with members:
    - "guide me interacting with deep networks"
    - "learning to discover and localize visual objects with open vocabulary"
    - "open vocabulary phrase detection"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "fast object class labelling via speech"
    - "context embedding networks"
    - "teaching machines to describe images via natural language feedback"
    - "who said what modeling individual labelers improves classification"

Connected through hyperedge HE-5 (induced by source paper "open vocabulary phrase detection"):
  with members:
    - "interpretable and globally optimal prediction for textual grounding using image concepts"
    - "discriminative bimodal networks for visual localization and detection with natural language queries"
    - "query guided regression network with context policy for phrase grounding"
    - "learning two branch neural networks for image text matching tasks"
    - "conditional image text embedding networks"
    - "comprehension guided referring expressions"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "deep image retrieval learning global representations for image search"

Connected through hyperedge HE-6 (induced by source paper "bilinear attention networks"):
  with members:
    - "learning to count objects in natural images for visual question answering"
    - "interpretable and globally optimal prediction for textual grounding using image concepts"
    - "mutan multimodal tucker fusion for visual question answering"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "interpretable counting for visual question answering"
    - "residual networks are exponential ensembles of relatively shallow networks"
    - "dual attention networks for multimodal reasoning and matching"

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)