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 bimodal networks for visual localization and detection with natural language queries"
  Abstract: Associating image regions with text queries has been recently explored as a new way to bridge visual and linguistic representations. A few pioneering approaches have been proposed based on recurrent neural language models trained generatively (e.g., generating captions), but achieving somewhat limited localization accuracy. To better address natural-language-based visual entity localization, we propose a discriminative approach. We formulate a discriminative bimodal neural network (DBNet), which can be trained by a classifier with extensive use of negative samples. Our training objective encourages better localization on single images, incorporates text phrases in a broad range, and properly pairs image regions with text phrases into positive and negative examples. Experiments on the Visual Genome dataset demonstrate the proposed DBNet significantly outperforms previous state-of-the-art methods both for localization on single images and for detection on multiple images. We we also establish an evaluation protocol for natural-language visual detection.

Connected through hyperedge HE-1 (induced by source paper "multi task collaborative network for joint referring expression comprehension and segmentation"):
  with members:
    - "a real time global inference network for one stage referring expression comprehension"
    - "retinamask learning to predict masks improves state of the art single shot detection for free"
    - "zero shot grounding of objects from natural language queries"
    - "cross modal self attention network for referring image segmentation"
    - "yolact real time instance segmentation"
    - "dynamic multimodal instance segmentation guided by natural language queries"
    - "neighbourhood watch referring expression comprehension via language guided graph attention networks"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-2 (induced by source paper "neural sequential phrase grounding seqground"):
  with members:
    - "iterative visual reasoning beyond convolutions"
    - "a neural multi sequence alignment technique neumatch"
    - "graph r cnn for scene graph generation"
    - "interpretable and globally optimal prediction for textual grounding using image concepts"
    - "mattnet modular attention network for referring expression comprehension"
    - "heterogeneous knowledge transfer in video emotion recognition attribution and summarization"
    - "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 "a fast and accurate one stage approach to visual grounding"):
  with members:
    - "improving referring expression grounding with cross modal attention guided erasing"
    - "neural sequential phrase grounding seqground"
    - "dynamic multimodal instance segmentation guided by natural language queries"
    - "tell and answer towards explainable visual question answering using attributes and captions"
    - "yolov3 an incremental improvement"
    - "interpretable and globally optimal prediction for textual grounding using image concepts"
    - "unsupervised image captioning"
    - "mattnet modular attention network for referring expression comprehension"

Connected through hyperedge HE-4 (induced by source paper "weakly supervised spatio temporally grounding natural sentence in video"):
  with members:
    - "using syntax to ground referring expressions in natural images"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "motion appearance co memory networks for video question answering"
    - "object referring in videos with language and human gaze"
    - "real time referring expression comprehension by single stage grounding network"
    - "mattnet modular attention network for referring expression comprehension"
    - "knowledge aided consistency for weakly supervised phrase grounding"
    - "weakly supervised video object grounding from text by loss weighting and object interaction"

Connected through hyperedge HE-5 (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"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "a real time global inference network for one stage referring expression comprehension"):
  with members:
    - "zero shot grounding of objects from natural language queries"
    - "referring relationships"
    - "yolov3 an incremental improvement"
    - "mattnet modular attention network for referring expression comprehension"
    - "rethinking diversified and discriminative proposal generation for visual grounding"
    - "query guided regression network with context policy for phrase grounding"
    - "grounding referring expressions in images by variational context"
    - "attention is all you need"

Connected through hyperedge HE-7 (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"
    - "query guided regression network with context policy for phrase grounding"
    - "conditional image text embedding networks"
    - "discriminative learning of open vocabulary object retrieval and localization by negative phrase augmentation"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "speed accuracy trade offs for modern convolutional object detectors"

Connected through hyperedge HE-8 (induced by source paper "video object segmentation with language referring expressions"):
  with members:
    - "interactive video object segmentation in the wild"
    - "object referring in videos with language and human gaze"
    - "interpretable and globally optimal prediction for textual grounding using image concepts"
    - "maskrnn instance level video object segmentation"
    - "mattnet modular attention network for referring expression comprehension"
    - "actor and action video segmentation from a sentence"
    - "the 2018 davis challenge on video object segmentation"
    - "query guided regression network with context policy for phrase grounding"

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)