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: "robust change captioning"
  Abstract: Describing what has changed in a scene can be useful to a user, but only if generated text focuses on what is semantically relevant. It is thus important to distinguish distractors (e.g. a viewpoint change) from relevant changes (e.g. an object has moved). We present a novel Dual Dynamic Attention Model (DUDA) to perform robust Change Captioning. Our model learns to distinguish distractors from semantic changes, localize the changes via Dual Attention over "before" and "after" images, and accurately describe them in natural language via Dynamic Speaker, by adaptively focusing on the necessary visual inputs (e.g. "before" or "after" image). To study the problem in depth, we collect a CLEVR-Change dataset, built off the CLEVR engine, with 5 types of scene changes. We benchmark a number of baselines on our dataset, and systematically study different change types and robustness to distractors. We show the superiority of our DUDA model in terms of both change captioning and localization. We also show that our approach is general, obtaining state-of-the-art results on the recent realistic Spot-the-Diff dataset which has no distractors.

Connected through hyperedge HE-1 (induced by source paper "say as you wish fine grained control of image caption generation with abstract scene graphs"):
  with members:
    - "graphical contrastive losses for scene graph parsing"
    - "sequential latent spaces for modeling the intention during diverse image captioning"
    - "ctrl a conditional transformer language model for controllable generation"
    - "describing like humans on diversity in image captioning"
    - "nocaps novel object captioning at scale"
    - "intention oriented image captions with guiding objects"
    - "neural baby talk"
    - "semstyle learning to generate stylised image captions using unaligned text"

Connected through hyperedge HE-2 (induced by source paper "semantic change pattern analysis"):
  with members:
    - "urban change detection for multispectral earth observation using convolutional neural networks"
    - "fully convolutional siamese networks for change detection"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "rethinking atrous convolution for semantic image segmentation"
    - "full resolution residual networks for semantic segmentation in street scenes"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "semantic change detection with hypermaps"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "fashion iq a new dataset towards retrieving images by natural language feedback"):
  with members:
    - "neural naturalist generating fine grained image comparisons"
    - "drill down interactive retrieval of complex scenes using natural language queries"
    - "expressing visual relationships via language"
    - "modanet a large scale street fashion dataset with polygon annotations"
    - "learning to describe differences between pairs of similar images"
    - "composing text and image for image retrieval an empirical odyssey"
    - "dialog based interactive image retrieval"
    - "person search with natural language description"

Connected through hyperedge HE-4 (induced by source paper "neural naturalist generating fine grained image comparisons"):
  with members:
    - "expressing visual relationships via language"
    - "learning to describe differences between pairs of similar images"
    - "a corpus for reasoning about natural language grounded in photographs"
    - "attention is all you need"
    - "context aware captions from context agnostic supervision"
    - "the inaturalist species classification and detection dataset"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "generating visual explanations"

Connected through hyperedge HE-5 (induced by source paper "blocksworld revisited learning and reasoning to generate event sequences from image pairs"):
  with members:
    - "modularized textual grounding for counterfactual resilience"
    - "learning to describe differences between pairs of similar images"
    - "large scale visual relationship understanding"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a simple neural network module for relational reasoning"
    - "mask r cnn"
    - "temporal relational reasoning in videos"

Connected through hyperedge HE-6 (induced by source paper "weakly supervised silhouette based semantic change detection"):
  with members:
    - "panoptic segmentation"
    - "m4cd a robust change detection method for intelligent visual surveillance"
    - "learning to measure change fully convolutional siamese metric networks for scene change detection"
    - "learning to adapt structured output space for semantic segmentation"
    - "learning to describe differences between pairs of similar images"
    - "fully convolutional siamese networks for change detection"
    - "high resolution semantic change detection"
    - "dense optical flow based change detection network robust to difference of camera viewpoints"

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)