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: "improving referring expression grounding with cross modal attention guided erasing"
  Abstract: Referring expression grounding aims at locating certain objects or persons in an image with a referring expression, where the key challenge is to comprehend and align various types of information from visual and textual domain, such as visual attributes, location and interactions with surrounding regions. Although the attention mechanism has been successfully applied for cross-modal alignments, previous attention models focus on only the most dominant features of both modalities, and neglect the fact that there could be multiple comprehensive textual-visual correspondences between images and referring expressions. To tackle this issue, we design a novel cross-modal attention-guided erasing approach, where we discard the most dominant information from either textual or visual domains to generate difficult training samples online, and to drive the model to discover complementary textual-visual correspondences. Extensive experiments demonstrate the effectiveness of our proposed method, which achieves state-of-the-art performance on three referring expression grounding datasets.

Connected through hyperedge HE-1 (induced by source paper "cops ref a new dataset and task on compositional referring expression comprehension"):
  with members:
    - "visual semantic reasoning for image text matching"
    - "clevr ref diagnosing visual reasoning with referring expressions"
    - "binary image selection bison interpretable evaluation of visual grounding"
    - "cross modal self attention network for referring image segmentation"
    - "neural sequential phrase grounding seqground"
    - "cross modal relationship inference for grounding referring expressions"
    - "neighbourhood watch referring expression comprehension via language guided graph attention networks"
    - "visual coreference resolution in visual dialog using neural module networks"

Connected through hyperedge HE-2 (induced by source paper "giving commands to a self driving car a multimodal reasoner for visual grounding"):
  with members:
    - "nuscenes a multimodal dataset for autonomous driving"
    - "boosting entity linking performance by leveraging unlabeled documents"
    - "talk2car taking control of your self driving car"
    - "objects as points"
    - "mattnet modular attention network for referring expression comprehension"
    - "compositional attention networks for machine reasoning"
    - "explainable neural computation via stack neural module networks"
    - "ddrprog a clevr differentiable dynamic reasoning programmer"

Connected through hyperedge HE-3 (induced by source paper "reverie remote embodied visual referring expression in real indoor environments"):
  with members:
    - "tactical rewind self correction via backtracking in vision and language navigation"
    - "the regretful agent heuristic aided navigation through progress estimation"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "talk the walk navigating new york city through grounded dialogue"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "speaker follower models for vision and language navigation"
    - "vision based navigation with language based assistance via imitation learning with indirect intervention"
    - "mattnet modular attention network for referring expression comprehension"

Connected through hyperedge HE-4 (induced by source paper "multi modality latent interaction network for visual question answering"):
  with members:
    - "weakly supervised compositional featureaggregation for few shot recognition"
    - "pay less attention with lightweight and dynamic convolutions"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "2nd place solution to the gqa challenge 2019"
    - "question type guided attention in visual question answering"
    - "bilinear attention networks"
    - "learning to count objects in natural images for visual question answering"
    - "deep contextualized word representations"

Connected through hyperedge HE-5 (induced by source paper "a fast and accurate one stage approach to visual grounding"):
  with members:
    - "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"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-6 (induced by source paper "camp cross modal adaptive message passing for text image retrieval"):
  with members:
    - "bilinear attention networks"
    - "stacked cross attention for image text matching"
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"
    - "conditional image text embedding networks"
    - "dual path convolutional image text embedding with instance loss"
    - "vse improving visual semantic embeddings with hard negatives"

Connected through hyperedge HE-7 (induced by source paper "deep self learning from noisy labels"):
  with members:
    - "curriculumnet weakly supervised learning from large scale web images"
    - "talking face generation by adversarially disentangled audio visual representation"
    - "joint optimization framework for learning with noisy labels"
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "learning to reweight examples for robust deep learning"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "using trusted data to train deep networks on labels corrupted by severe noise"
    - "a semi supervised two stage approach to learning from noisy labels"

Connected through hyperedge HE-8 (induced by source paper "revisiting visual grounding"):
  with members:
    - "differentiable scene graphs"
    - "referring relationships"
    - "using syntax to ground referring expressions in natural images"
    - "interpretable and globally optimal prediction for textual grounding using image concepts"
    - "visual referring expression recognition what do systems actually learn"
    - "open vocabulary phrase detection"
    - "grounding referring expressions in images by variational context"
    - "semantic image retrieval via active grounding of visual situations"

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)