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: "interpretable and globally optimal prediction for textual grounding using image concepts"
  Abstract: Textual grounding is an important but challenging task for human-computer interaction, robotics and knowledge mining. Existing algorithms generally formulate the task as selection from a set of bounding box proposals obtained from deep net based systems. In this work, we demonstrate that we can cast the problem of textual grounding into a unified framework that permits efficient search over all possible bounding boxes. Hence, the method is able to consider significantly more proposals and doesn't rely on a successful first stage hypothesizing bounding box proposals. Beyond, we demonstrate that the trained parameters of our model can be used as word-embeddings which capture spatial-image relationships and provide interpretability. Lastly, at the time of submission, our approach outperformed the current state-of-the-art methods on the Flickr 30k Entities and the ReferItGame dataset by 3.08% and 7.77% respectively.

Connected through hyperedge HE-1 (induced by source paper "modularized textual grounding for counterfactual resilience"):
  with members:
    - "weakly supervised attention learning for textual phrases grounding"
    - "w talc weakly supervised temporal activity localization and classification"
    - "mattnet modular attention network for referring expression comprehension"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "generating counterfactual explanations with natural language"
    - "exploring the limits of weakly supervised pretraining"
    - "unsupervised textual grounding linking words to image concepts"
    - "explainable neural computation via stack neural module networks"

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"
    - "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"
    - "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"
    - "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-4 (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"
    - "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"
    - "conditional image text embedding networks"

Connected through hyperedge HE-5 (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"
    - "mattnet modular attention network for referring expression comprehension"
    - "grounding referring expressions in images by variational context"

Connected through hyperedge HE-6 (induced by source paper "learning cross modal context graph for visual grounding"):
  with members:
    - "trends in integration of vision and language research a survey of tasks datasets and methods"
    - "neural sequential phrase grounding seqground"
    - "language conditioned graph networks for relational reasoning"
    - "murel multimodal relational reasoning for visual question answering"
    - "neighbourhood watch referring expression comprehension via language guided graph attention networks"
    - "graph r cnn for scene graph generation"
    - "unsupervised image captioning"
    - "mattnet modular attention network for referring expression comprehension"

Connected through hyperedge HE-7 (induced by source paper "joint wasserstein autoencoders for aligning multimodal embeddings"):
  with members:
    - "stacked cross attention for image text matching"
    - "vse ens visual semantic embeddings with efficient negative sampling"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"
    - "multimodal machine learning a survey and taxonomy"
    - "cycada cycle consistent adversarial domain adaptation"
    - "learning two branch neural networks for image text matching tasks"
    - "conditional image text embedding networks"
    - "learning robust visual semantic embeddings"

Connected through hyperedge HE-8 (induced by source paper "revisiting visual grounding"):
  with members:
    - "improving referring expression grounding with cross modal attention guided erasing"
    - "differentiable scene graphs"
    - "referring relationships"
    - "using syntax to ground referring expressions in natural images"
    - "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)