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: "recurrent multimodal interaction for referring image segmentation"
  Abstract: In this paper we are interested in the problem of image segmentation given natural language descriptions, i.e. referring expressions. Existing works tackle this problem by first modeling images and sentences independently and then segment images by combining these two types of representations. We argue that learning word-to-image interaction is more native in the sense of jointly modeling two modalities for the image segmentation task, and we propose convolutional multimodal LSTM to encode the sequential interactions between individual words, visual information, and spatial information. We show that our proposed model outperforms the baseline model on benchmark datasets. In addition, we analyze the intermediate output of the proposed multimodal LSTM approach and empirically explain how this approach enforces a more effective word-to-image interaction.

Connected through hyperedge HE-1 (induced by source paper "dual convolutional lstm network for referring image segmentation"):
  with members:
    - "cross modal self attention network for referring image segmentation"
    - "dynamic multimodal instance segmentation guided by natural language queries"
    - "mattnet modular attention network for referring expression comprehension"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "fcn rlstm deep spatio temporal neural networks for vehicle counting in city cameras"
    - "mask r cnn"
    - "segmentation from natural language expressions"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"

Connected through hyperedge HE-2 (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-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 "clevr ref diagnosing visual reasoning with referring expressions"):
  with members:
    - "dynamic multimodal instance segmentation guided by natural language queries"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "mattnet modular attention network for referring expression comprehension"
    - "visual referring expression recognition what do systems actually learn"
    - "compositional attention networks for machine reasoning"
    - "explainable neural computation via stack neural module networks"
    - "inferring and executing programs for visual reasoning"
    - "a simple neural network module for relational reasoning"

Connected through hyperedge HE-5 (induced by source paper "referring expression object segmentation with caption aware consistency"):
  with members:
    - "dynamic multimodal instance segmentation guided by natural language queries"
    - "mattnet modular attention network for referring expression comprehension"
    - "grounding referring expressions in images by variational context"
    - "comprehension guided referring expressions"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "modeling relationships in referential expressions with compositional modular networks"
    - "a joint speaker listener reinforcer model for referring expressions"
    - "segmentation from natural language expressions"

Connected through hyperedge HE-6 (induced by source paper "cross modal self attention network for referring image segmentation"):
  with members:
    - "dynamic multimodal instance segmentation guided by natural language queries"
    - "mattnet modular attention network for referring expression comprehension"
    - "non local neural networks"
    - "attention is all you need"
    - "modeling relationships in referential expressions with compositional modular networks"
    - "segmentation from natural language expressions"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "pyramid scene parsing network"

Connected through hyperedge HE-7 (induced by source paper "scene graph parsing by attention graph"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "scene graph parsing as dependency parsing"
    - "attention is all you need"
    - "arc swift a novel transition system for dependency parsing"
    - "attention correctness in neural image captioning"
    - "spice semantic propositional image caption evaluation"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"

Connected through hyperedge HE-8 (induced by source paper "smit stochastic multi label image to image translation"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "dynamic multimodal instance segmentation guided by natural language queries"
    - "recycle gan unsupervised video retargeting"
    - "the relativistic discriminator a key element missing from standard gan"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "multimodal unsupervised image to image translation"
    - "exemplar guided unsupervised image to image translation with semantic consistency"
    - "spectral normalization for generative adversarial 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)