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: "learning to generalize to new compositions in image understanding"
  Abstract: Recurrent neural networks have recently been used for learning to describe images using natural language. However, it has been observed that these models generalize poorly to scenes that were not observed during training, possibly depending too strongly on the statistics of the text in the training data. Here we propose to describe images using short structured representations, aiming to capture the crux of a description. These structured representations allow us to tease-out and evaluate separately two types of generalization: standard generalization to new images with similar scenes, and generalization to new combinations of known entities. We compare two learning approaches on the MS-COCO dataset: a state-of-the-art recurrent network based on an LSTM (Show, Attend and Tell), and a simple structured prediction model on top of a deep network. We find that the structured model generalizes to new compositions substantially better than the LSTM, ~7 times the accuracy of predicting structured representations. By providing a concrete method to quantify generalization for unseen combinations, we argue that structured representations and compositional splits are a useful benchmark for image captioning, and advocate compositional models that capture linguistic and visual structure.

Connected through hyperedge HE-1 (induced by source paper "learning of colors from color names distribution and point estimation"):
  with members:
    - "neural network with unbounded activation functions is universal approximator"
    - "colors in context a pragmatic neural model for grounded language understanding"
    - "learning to generate compositional color descriptions"
    - "enriching word vectors with subword information"
    - "generative adversarial text to image synthesis"
    - "character sequence models for colorfulwords"
    - "pixel recurrent neural networks"
    - "generating images from captions with attention"

Connected through hyperedge HE-2 (induced by source paper "compositional generalization in image captioning"):
  with members:
    - "the curious case of neural text degeneration"
    - "universal dependency parsing from scratch"
    - "nocaps novel object captioning at scale"
    - "neural baby talk"
    - "assessing composition in sentence vector representations"
    - "hierarchical neural story generation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "rnns implicitly implement tensor product representations"

Connected through hyperedge HE-3 (induced by source paper "shuffle then assemble learning object agnostic visual relationship features"):
  with members:
    - "learning to adapt structured output space for semantic segmentation"
    - "vqa e explaining elaborating and enhancing your answers for visual questions"
    - "context aware visual policy network for sequence level image captioning"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "zero shot visual recognition using semantics preserving adversarial embedding networks"
    - "vip cnn visual phrase guided convolutional neural network"

Connected through hyperedge HE-4 (induced by source paper "domain aware generalized zero shot learning"):
  with members:
    - "domain invariant projection learning for zero shot recognition"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "multi modal cycle consistent generalized zero shot learning"
    - "zero shot kernel learning"
    - "preserving semantic relations for zero shot learning"
    - "probabilistic and or attribute grouping for zero shot learning"
    - "model selection for generalized zero shot learning"
    - "transductive unbiased embedding for zero shot learning"

Connected through hyperedge HE-5 (induced by source paper "detecting unseen visual relations using analogies"):
  with members:
    - "ican instance centric attention network for human object interaction detection"
    - "large scale visual relationship understanding"
    - "learning human object interactions by graph parsing neural networks"
    - "detecting and recognizing human object interactions"
    - "a simple neural network module for relational reasoning"
    - "weakly supervised learning of visual relations"
    - "detecting visual relationships with deep relational networks"
    - "relnet end to end modeling of entities relations"

Connected through hyperedge HE-6 (induced by source paper "attentive relational networks for mapping images to scene graphs"):
  with members:
    - "referring relationships"
    - "linknet relational embedding for scene graph"
    - "shuffle then assemble learning object agnostic visual relationship features"
    - "graph r cnn for scene graph generation"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "factorizable net an efficient subgraph based framework for scene graph generation"
    - "exploring visual relationship for image captioning"
    - "vip cnn visual phrase guided convolutional neural network"

Connected through hyperedge HE-7 (induced by source paper "context dependent diffusion network for visual relationship detection"):
  with members:
    - "tensor graph convolutional neural network"
    - "action attending graphic neural network"
    - "when work matters transforming classical network structures to graph cnn"
    - "walk steered convolution for graph classification"
    - "spatio temporal graph convolution for skeleton based action recognition"
    - "vip cnn visual phrase guided convolutional neural network"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "weakly supervised learning of visual relations"

Connected through hyperedge HE-8 (induced by source paper "adaptive confidence smoothing for generalized zero shot learning"):
  with members:
    - "domain invariant projection learning for zero shot recognition"
    - "multi modal cycle consistent generalized zero shot learning"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "zero shot kernel learning"
    - "probabilistic and or attribute grouping for zero shot learning"
    - "model selection for generalized zero shot learning"
    - "transductive unbiased embedding for zero shot learning"
    - "enhancing the reliability of out of distribution image detection in neural 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)