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: "sherlock scalable fact learning in images"
  Abstract: We study scalable and uniform understanding of facts in images. Existing visual recognition systems are typically modeled differently for each fact type such as objects, actions, and interactions. We propose a setting where all these facts can be modeled simultaneously with a capacity to understand unbounded number of facts in a structured way. The training data comes as structured facts in images, including (1) objects (e.g., $ $), (2) attributes (e.g., $ $), (3) actions (e.g., $ $), and (4) interactions (e.g., $ $). Each fact has a semantic language view (e.g., $ $) and a visual view (an image with this fact). We show that learning visual facts in a structured way enables not only a uniform but also generalizable visual understanding. We propose and investigate recent and strong approaches from the multiview learning literature and also introduce two learning representation models as potential baselines. We applied the investigated methods on several datasets that we augmented with structured facts and a large scale dataset of more than 202,000 facts and 814,000 images. Our experiments show the advantage of relating facts by the structure by the proposed models compared to the designed baselines on bidirectional fact retrieval.

Connected through hyperedge HE-1 (induced by source paper "continual learning in neural networks"):
  with members:
    - "class incremental learning via deep model consolidation"
    - "knowledge transfer in deep block modular neural networks"
    - "meta learning representations for continual learning"
    - "continual reinforcement learning in 3d non stationary environments"
    - "continual learning with tiny episodic memories"
    - "continual classification learning using generative models"
    - "task free continual learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"

Connected through hyperedge HE-2 (induced by source paper "large scale visual relationship understanding"):
  with members:
    - "graphical contrastive losses for scene graph parsing"
    - "introduction to the 1st place winning model of openimages relationship detection challenge"
    - "graph r cnn for scene graph generation"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "an interpretable model for scene graph generation"
    - "vip cnn visual phrase guided convolutional neural network"
    - "neural motifs scene graph parsing with global context"
    - "detecting visual relationships with deep relational networks"

Connected through hyperedge HE-3 (induced by source paper "factorizable net an efficient subgraph based framework for scene graph generation"):
  with members:
    - "vip cnn visual phrase guided convolutional neural network"
    - "co attending free form regions and detections with multi modal multiplicative feature embedding for visual question answering"
    - "towards context aware interaction recognition"
    - "visual question generation as dual task of visual question answering"
    - "weakly supervised learning of visual relations"
    - "detecting visual relationships with deep relational networks"
    - "scene graph generation by iterative message passing"
    - "visual relationship detection with internal and external linguistic knowledge distillation"

Connected through hyperedge HE-4 (induced by source paper "exploring the challenges towards lifelong fact learning"):
  with members:
    - "growing a brain fine tuning by increasing model capacity"
    - "large scale visual relationship understanding"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"
    - "memory aware synapses learning what not to forget"
    - "core50 a new dataset and benchmark for continuous object recognition"
    - "encoder based lifelong learning"
    - "continual learning through synaptic intelligence"
    - "overcoming catastrophic forgetting by incremental moment matching"

Connected through hyperedge HE-5 (induced by source paper "weakly supervised learning of visual relations"):
  with members:
    - "learning from video and text via large scale discriminative clustering"
    - "minding the gaps for block frank wolfe optimization of structured svms"
    - "grounding of textual phrases in images by reconstruction"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "a multipath network for object detection"
    - "visual relationship detection with language priors"
    - "captioning images with diverse objects"
    - "latent embeddings for zero shot classification"

Connected through hyperedge HE-6 (induced by source paper "detecting visual relationships with deep relational networks"):
  with members:
    - "places an image database for deep scene understanding"
    - "spice semantic propositional image caption evaluation"
    - "grounding of textual phrases in images by reconstruction"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "visual question answering a survey of methods and datasets"
    - "deep markov random field for image modeling"
    - "visual relationship detection with language priors"
    - "from images to sentences through scene description graphs using commonsense reasoning and knowledge"

Connected through hyperedge HE-7 (induced by source paper "memory aware synapses learning what not to forget"):
  with members:
    - "progressive neural networks"
    - "encoder based lifelong learning"
    - "overcoming catastrophic forgetting by incremental moment matching"
    - "incremental learning of object detectors without catastrophic forgetting"
    - "gradient episodic memory for continual learning"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "learning to learn by gradient descent by gradient descent"

Connected through hyperedge HE-8 (induced by source paper "automatic annotation of structured facts in images"):
  with members:
    - "exploring models and data for image question answering"
    - "vqa visual question answering"
    - "show attend and tell neural image caption generation with visual attention"
    - "flickr30k entities collecting region to phrase correspondences for richer image to sentence models"
    - "ask your neurons a neural based approach to answering questions about images"
    - "unifying visual semantic embeddings with multimodal neural language models"
    - "deep captioning with multimodal recurrent neural networks m rnn"
    - "deep fragment embeddings for bidirectional image sentence mapping"

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)