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: "open vocabulary scene parsing"
  Abstract: Recognizing arbitrary objects in the wild has been a challenging problem due to the limitations of existing classification models and datasets. In this paper, we propose a new task that aims at parsing scenes with a large and open vocabulary, and several evaluation metrics are explored for this problem. Our proposed approach to this problem is a joint image pixel and word concept embeddings framework, where word concepts are connected by semantic relations. We validate the open vocabulary prediction ability of our framework on ADE20K dataset which covers a wide variety of scenes and objects. We further explore the trained joint embedding space to show its interpretability.

Connected through hyperedge HE-1 (induced by source paper "procedure planning in instructional videos"):
  with members:
    - "cross task weakly supervised learning from instructional videos"
    - "videobert a joint model for video and language representation learning"
    - "coin a large scale dataset for comprehensive instructional video analysis"
    - "d3tw discriminative differentiable dynamic time warping for weakly supervised action alignment and segmentation"
    - "howto100m learning a text video embedding by watching hundred million narrated video clips"
    - "universal planning networks"
    - "learning latent dynamics for planning from pixels"
    - "learning plannable representations with causal infogan"

Connected through hyperedge HE-2 (induced by source paper "a study on action detection in the wild"):
  with members:
    - "large scale fine grained categorization and domain specific transfer learning"
    - "video action transformer network"
    - "a structured model for action detection"
    - "slowfast networks for video recognition"
    - "d3d distilled 3d networks for video action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "non local neural networks"
    - "focal loss for dense object detection"

Connected through hyperedge HE-3 (induced by source paper "natural vocabulary emerges from free form annotations"):
  with members:
    - "guide me interacting with deep networks"
    - "learning to discover and localize visual objects with open vocabulary"
    - "open vocabulary phrase detection"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "fast object class labelling via speech"
    - "context embedding networks"
    - "teaching machines to describe images via natural language feedback"
    - "who said what modeling individual labelers improves classification"

Connected through hyperedge HE-4 (induced by source paper "recent advances in open set recognition a survey"):
  with members:
    - "open world learning and application to product classification"
    - "large scale long tailed recognition in an open world"
    - "c2ae class conditioned auto encoder for open set recognition"
    - "bayesian semantic instance segmentation in open set world"
    - "learning a neural network based representation for open set recognition"
    - "open category detection with pac guarantees"
    - "open set domain adaptation by backpropagation"
    - "reducing network agnostophobia"

Connected through hyperedge HE-5 (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-6 (induced by source paper "dynamic structured semantic propagation network"):
  with members:
    - "deep learning with dynamic computation graphs"
    - "deep learning for precipitation nowcasting a benchmark and a new model"
    - "deep variation structured reinforcement learning for visual relationship and attribute detection"
    - "interpretable structure evolving lstm"
    - "labelbank revisiting global perspectives for semantic segmentation"
    - "one model to learn them all"
    - "semantic object parsing with graph lstm"
    - "semantic understanding of scenes through the ade20k dataset"

Connected through hyperedge HE-7 (induced by source paper "hierarchical novelty detection for visual object recognition"):
  with members:
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "dropout inference in bayesian neural networks with alpha divergences"
    - "zero shot learning a comprehensive evaluation of the good the bad and the ugly"
    - "learning a deep embedding model for zero shot learning"
    - "synthesized classifiers for zero shot learning"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"
    - "semi supervised vocabulary informed learning"
    - "an empirical study and analysis of generalized zero shot learning for object recognition in the wild"

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)