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: "taskonomy disentangling task transfer learning"
  Abstract: Do visual tasks have a relationship, or are they unrelated? For instance, could having surface normals simplify estimating the depth of an image? Intuition answers these questions positively, implying existence of a structure among visual tasks. Knowing this structure has notable values; it is the concept underlying transfer learning and provides a principled way for identifying redundancies across tasks, e.g., to seamlessly reuse supervision among related tasks or solve many tasks in one system without piling up the complexity. #R##N#We proposes a fully computational approach for modeling the structure of space of visual tasks. This is done via finding (first and higher-order) transfer learning dependencies across a dictionary of twenty six 2D, 2.5D, 3D, and semantic tasks in a latent space. The product is a computational taxonomic map for task transfer learning. We study the consequences of this structure, e.g. nontrivial emerged relationships, and exploit them to reduce the demand for labeled data. For example, we show that the total number of labeled datapoints needed for solving a set of 10 tasks can be reduced by roughly 2/3 (compared to training independently) while keeping the performance nearly the same. We provide a set of tools for computing and probing this taxonomical structure including a solver that users can employ to devise efficient supervision policies for their use cases.

Connected through hyperedge HE-1 (induced by source paper "mti net multi scale task interaction networks for multi task learning"):
  with members:
    - "which tasks should be learned together in multi task learning"
    - "branched multi task networks deciding what layers to share"
    - "deep high resolution representation learning for visual recognition"
    - "pattern affinitive propagation across depth surface normal and semantic segmentation"
    - "deep high resolution representation learning for human pose estimation"
    - "panoptic feature pyramid networks"
    - "attentive single tasking of multiple tasks"
    - "a modulation module for multi task learning with applications in image retrieval"

Connected through hyperedge HE-2 (induced by source paper "on leveraging pretrained gans for limited data generation"):
  with members:
    - "infinite brain mr images pggan based data augmentation for tumor detection"
    - "transfer learning in biomedical natural language processing an evaluation of bert and elmo on ten benchmarking datasets"
    - "learning more with less conditional pggan based data augmentation for brain metastases detection using highly rough annotation on mr images"
    - "open event extraction from online text using a generative adversarial network"
    - "analyzing and improving the image quality of stylegan"
    - "generative adversarial network in medical imaging a review"
    - "a bert based transfer learning approach for hate speech detection in online social media"
    - "image generation from small datasets via batch statistics adaptation"

Connected through hyperedge HE-3 (induced by source paper "domain decluttering simplifying images to mitigate synthetic real domain shift and improve depth estimation"):
  with members:
    - "modularized textual grounding for counterfactual resilience"
    - "geometry aware symmetric domain adaptation for monocular depth estimation"
    - "learning to adapt structured output space for semantic segmentation"
    - "dropblock a regularization method for convolutional networks"
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "deep ordinal regression network for monocular depth estimation"
    - "t2net synthetic to realistic translation for solving single image depth estimation tasks"
    - "learning monocular depth by distilling cross domain stereo networks"

Connected through hyperedge HE-4 (induced by source paper "evolving losses for unsupervised video representation learning"):
  with members:
    - "learning correspondence from the cycle consistency of time"
    - "local aggregation for unsupervised learning of visual embeddings"
    - "moments in time dataset one million videos for event understanding"
    - "deep clustering for unsupervised learning of visual features"
    - "unsupervised learning via meta learning"
    - "autoaugment learning augmentation policies from data"
    - "slowfast networks for video recognition"
    - "self supervised video representation learning with space time cubic puzzles"

Connected through hyperedge HE-5 (induced by source paper "leep a new measure to evaluate transferability of learned representations"):
  with members:
    - "dataset2vec learning dataset meta features"
    - "gluoncv and gluonnlp deep learning in computer vision and natural language processing"
    - "a baseline for few shot image classification"
    - "which tasks should be learned together in multi task learning"
    - "p2l predicting transfer learning for images and semantic relations"
    - "transferability and hardness of supervised classification tasks"
    - "task2vec task embedding for meta learning"
    - "fixynn efficient hardware for mobile computer vision via transfer learning"

Connected through hyperedge HE-6 (induced by source paper "dynamic task weighting methods for multi task networks in autonomous driving systems"):
  with members:
    - "multinet multi stream feature aggregation and geometric loss strategy for multi task learning"
    - "real time joint object detection and semantic segmentation network for automated driving"
    - "which tasks should be learned together in multi task learning"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "soilingnet soiling detection on automotive surround view cameras"
    - "autoaugment learning augmentation policies from data"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "end to end multi task learning with attention"

Connected through hyperedge HE-7 (induced by source paper "task assisted domain adaptation with anchor tasks"):
  with members:
    - "domain adaptation for structured output via discriminative patch representations"
    - "learning to adapt structured output space for semantic segmentation"
    - "cross domain weakly supervised object detection through progressive domain adaptation"
    - "3d human pose estimation in the wild by adversarial learning"
    - "regularizing deep networks by modeling and predicting label structure"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-8 (induced by source paper "gradient surgery for multi task learning"):
  with members:
    - "distilling policy distillation"
    - "regularized hierarchical policies for compositional transfer in robotics"
    - "branched multi task networks deciding what layers to share"
    - "meta world a benchmark and evaluation for multi task and meta reinforcement learning"
    - "routing networks and the challenges of modular and compositional computation"
    - "ray interference a source of plateaus in deep reinforcement learning"
    - "attentive single tasking of multiple tasks"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"

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)