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: "commonly uncommon semantic sparsity in situation recognition"
  Abstract: Semantic sparsity is a common challenge in structured visual classification problems; when the output space is complex, the vast majority of the possible predictions are rarely, if ever, seen in the training set. This paper studies semantic sparsity in situation recognition, the task of producing structured summaries of what is happening in images, including activities, objects and the roles objects play within the activity. For this problem, we find empirically that most object-role combinations are rare, and current state-of-the-art models significantly underperform in this sparse data regime. We avoid many such errors by (1) introducing a novel tensor composition function that learns to share examples across role-noun combinations and (2) semantically augmenting our training data with automatically gathered examples of rarely observed outputs using web data. When integrated within a complete CRF-based structured prediction model, the tensor-based approach outperforms existing state of the art by a relative improvement of 2.11% and 4.40% on top-5 verb and noun-role accuracy, respectively. Adding 5 million images with our semantic augmentation techniques gives further relative improvements of 6.23% and 9.57% on top-5 verb and noun-role accuracy.

Connected through hyperedge HE-1 (induced by source paper "laso label set operations networks for multi label few shot learning"):
  with members:
    - "compositional gan learning conditional image composition"
    - "attributes as operators factorizing unseen attribute object compositions"
    - "deep meta learning learning to learn in the concept space"
    - "the elephant in the room"
    - "compositional gan learning image conditional binary composition"
    - "low shot learning from imaginary data"
    - "prototypical networks for few shot learning"
    - "multi label zero shot learning with structured knowledge graphs"

Connected through hyperedge HE-2 (induced by source paper "cross task weakly supervised learning from instructional videos"):
  with members:
    - "scaling egocentric vision the epic kitchens dataset"
    - "unsupervised learning and segmentation of complex activities from video"
    - "deep clustering for unsupervised learning of visual features"
    - "unsupervised visual linguistic reference resolution in instructional videos"
    - "joint discovery of object states and manipulation actions"
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "action sets weakly supervised action segmentation without ordering constraints"

Connected through hyperedge HE-3 (induced by source paper "learning taxonomies of concepts and not words using contextualized word representations a position paper"):
  with members:
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "hearst patterns revisited automatic hypernym detection from large text corpora"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "taxonomy induction using hypernym subsequences"
    - "distributional inclusion vector embedding for unsupervised hypernymy detection"
    - "hyperlex a large scale evaluation of graded lexical entailment"
    - "hypernyms under siege linguistically motivated artillery for hypernymy detection"

Connected through hyperedge HE-4 (induced by source paper "text2scene generating compositional scenes from textual descriptions"):
  with members:
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "chatpainter improving text to image generation using dialogue"
    - "imagine this scripts to compositions to videos"
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "semi parametric image synthesis"
    - "image generation from scene graphs"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "obj2text generating visually descriptive language from object layouts"

Connected through hyperedge HE-5 (induced by source paper "situation recognition with graph neural networks"):
  with members:
    - "teaching machines to describe images via natural language feedback"
    - "annotating object instances with a polygon rnn"
    - "visual translation embedding network for visual relation detection"
    - "semantic object parsing with graph lstm"
    - "gated graph sequence neural networks"
    - "places an image database for deep scene understanding"
    - "semi supervised classification with graph convolutional networks"
    - "the more you know using knowledge graphs for image classification"

Connected through hyperedge HE-6 (induced by source paper "recurrent models for situation recognition"):
  with members:
    - "structured attention networks"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "image captioning with semantic attention"
    - "spice semantic propositional image caption evaluation"
    - "boosting image captioning with attributes"
    - "learning models for actions and person object interactions with transfer to question answering"
    - "describing common human visual actions in images"
    - "language models for image captioning the quirks and what works"

Connected through hyperedge HE-7 (induced by source paper "moviegraphs towards understanding human centric situations from videos"):
  with members:
    - "situation recognition with graph neural networks"
    - "scene graph generation by iterative message passing"
    - "tgif qa toward spatio temporal reasoning in visual question answering"
    - "deepstory video story qa by deep embedded memory networks"
    - "a persona based neural conversation model"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "places an image database for deep scene understanding"
    - "marioqa answering questions by watching gameplay videos"

Connected through hyperedge HE-8 (induced by source paper "men also like shopping reducing gender bias amplification using corpus level constraints"):
  with members:
    - "semantics derived automatically from language corpora contain human like biases"
    - "equality of opportunity in supervised learning"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "vqa visual question answering"
    - "a survey on measuring indirect discrimination in machine learning"
    - "microsoft coco captions data collection and evaluation server"
    - "very deep convolutional networks for large scale image recognition"
    - "microsoft coco common objects in context"

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)