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: "conditional image text embedding networks"
  Abstract: This paper presents an approach for grounding phrases in images which jointly learns multiple text-conditioned embeddings in a single end-to-end model. In order to differentiate text phrases into semantically distinct subspaces, we propose a concept weight branch that automatically assigns phrases to embeddings, whereas prior works predefine such assignments. Our proposed solution simplifies the representation requirements for individual embeddings and allows the underrepresented concepts to take advantage of the shared representations before feeding them into concept-specific layers. Comprehensive experiments verify the effectiveness of our approach across three phrase grounding datasets, Flickr30K Entities, ReferIt Game, and Visual Genome, where we obtain a (resp.) 4%, 3%, and 4% improvement in grounding performance over a strong region-phrase embedding baseline.

Connected through hyperedge HE-1 (induced by source paper "scanrefer 3d object localization in rgb d scans using natural language"):
  with members:
    - "learning object bounding boxes for 3d instance segmentation on point clouds"
    - "3d bird s eye view instance segmentation"
    - "dynamic graph attention for referring expression comprehension"
    - "embodied language grounding with implicit 3d visual feature representations"
    - "align2ground weakly supervised phrase grounding guided by image caption alignment"
    - "rerere remote embodied referring expressions in real indoor environments"
    - "zero shot grounding of objects from natural language queries"
    - "cross modal self attention network for referring image segmentation"

Connected through hyperedge HE-2 (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-3 (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"
    - "discriminative bimodal networks for visual localization and detection with natural language queries"
    - "query guided regression network with context policy for phrase grounding"

Connected through hyperedge HE-4 (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-5 (induced by source paper "improving referring expression grounding with cross modal attention guided erasing"):
  with members:
    - "bilinear attention networks"
    - "dropblock a regularization method for convolutional networks"
    - "stacked cross attention for image text matching"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "adversarial complementary learning for weakly supervised object localization"
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "mattnet modular attention network for referring expression comprehension"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"

Connected through hyperedge HE-6 (induced by source paper "zero shot grounding of objects from natural language queries"):
  with members:
    - "block bilinear superdiagonal fusion for visual question answering and visual relationship detection"
    - "interpretable visual question answering by visual grounding from attention supervision mining"
    - "neural baby talk"
    - "graph r cnn for scene graph generation"
    - "yolov3 an incremental improvement"
    - "mattnet modular attention network for referring expression comprehension"
    - "rethinking diversified and discriminative proposal generation for visual grounding"
    - "knowledge aided consistency for weakly supervised phrase grounding"

Connected through hyperedge HE-7 (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-8 (induced by source paper "a real time cross modality correlation filtering method for referring expression comprehension"):
  with members:
    - "dynamic graph attention for referring expression comprehension"
    - "centernet keypoint triplets for object detection"
    - "objects as points"
    - "cross modal relationship inference for grounding referring expressions"
    - "cornernet detecting objects as paired keypoints"
    - "neighbourhood watch referring expression comprehension via language guided graph attention networks"
    - "interpretable and globally optimal prediction for textual grounding using image concepts"
    - "mattnet modular attention network for referring expression comprehension"

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)