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: "context embedding networks"
  Abstract: Low dimensional embeddings that capture the main variations of interest in collections of data are important for many applications. One way to construct these embeddings is to acquire estimates of similarity from the crowd. However, similarity is a multi-dimensional concept that varies from individual to individual. Existing models for learning embeddings from the crowd typically make simplifying assumptions such as all individuals estimate similarity using the same criteria, the list of criteria is known in advance, or that the crowd workers are not influenced by the data that they see. To overcome these limitations we introduce Context Embedding Networks (CENs). In addition to learning interpretable embeddings from images, CENs also model worker biases for different attributes along with the visual context i.e. the visual attributes highlighted by a set of images. Experiments on two noisy crowd annotated datasets show that modeling both worker bias and visual context results in more interpretable embeddings compared to existing approaches.

Connected through hyperedge HE-1 (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"
    - "teaching machines to describe images via natural language feedback"
    - "who said what modeling individual labelers improves classification"
    - "open vocabulary scene parsing"

Connected through hyperedge HE-2 (induced by source paper "guided feature selection for deep visual odometry"):
  with members:
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "context aware deep feature compression for high speed visual tracking"
    - "taskonomy disentangling task transfer learning"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "context encoding for semantic segmentation"
    - "vinet visual inertial odometry as a sequence to sequence learning problem"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "unsupervised learning of depth and ego motion from video"

Connected through hyperedge HE-3 (induced by source paper "teaching categories to human learners with visual explanations"):
  with members:
    - "interpretable convolutional neural networks"
    - "the inaturalist species classification and detection dataset"
    - "generating visual explanations"
    - "why should i trust you explaining the predictions of any classifier"
    - "becoming the expert interactive multi class machine teaching"
    - "deep residual learning for image recognition"
    - "learning deep features for discriminative localization"
    - "adam a method for stochastic optimization"

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.LG(Machine Learning)