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: "modeling uncertainty with hedged instance embedding"
  Abstract: Instance embeddings are an efficient and versatile image representation that facilitates applications like recognition, verification, retrieval, and clustering. Many metric learning methods represent the input as a single point in the embedding space. Often the distance between points is used as a proxy for match confidence. However, this can fail to represent uncertainty arising when the input is ambiguous, e.g., due to occlusion or blurriness. This work addresses this issue and explicitly models the uncertainty by hedging the location of each input in the embedding space. We introduce the hedged instance embedding (HIB) in which embeddings are modeled as random variables and the model is trained under the variational information bottleneck principle. Empirical results on our new N-digit MNIST dataset show that our method leads to the desired behavior of hedging its bets across the embedding space upon encountering ambiguous inputs. This results in improved performance for image matching and classification tasks, more structure in the learned embedding space, and an ability to compute a per-exemplar uncertainty measure that is correlated with downstream performance.

Connected through hyperedge HE-1 (induced by source paper "automatic differentiation variational inference with mixtures"):
  with members:
    - "divide and couple using monte carlo variational objectives for posterior approximation"
    - "implicit reparameterization gradients"
    - "on calibration of modern neural networks"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "vae with a vampprior"
    - "stochastic backpropagation through mixture density distributions"
    - "deep variational information bottleneck"
    - "importance weighted autoencoders"

Connected through hyperedge HE-2 (induced by source paper "polysemous visual semantic embedding for cross modal retrieval"):
  with members:
    - "on the convergence of adam and beyond"
    - "image2gif generating cinemagraphs using recurrent deep q networks"
    - "stacked cross attention for image text matching"
    - "cross modal retrieval with implicit concept association"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"
    - "attention is all you need"
    - "learning robust visual semantic embeddings"
    - "learning semantic concepts and order for image and sentence matching"

Connected through hyperedge HE-3 (induced by source paper "probabilistic face embeddings"):
  with members:
    - "striking the right balance with uncertainty"
    - "video face recognition component wise feature aggregation network c fan"
    - "cosface large margin cosine loss for deep face recognition"
    - "multi task convolutional neural network for pose invariant face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "towards interpretable face recognition"
    - "multicolumn networks for face recognition"
    - "additive margin softmax for face verification"

Connected through hyperedge HE-4 (induced by source paper "improving back translation with uncertainty based confidence estimation"):
  with members:
    - "openkiwi an open source framework for quality estimation"
    - "investigating backtranslation in neural machine translation"
    - "quantifying uncertainties in natural language processing tasks"
    - "achieving human parity on automatic chinese to english news translation"
    - "confidence modeling for neural semantic parsing"
    - "triangular architecture for rare language translation"
    - "phrase based neural unsupervised machine translation"
    - "understanding back translation at scale"

Connected through hyperedge HE-5 (induced by source paper "view invariant probabilistic embedding for human pose"):
  with members:
    - "cross view fusion for 3d human pose estimation"
    - "learnable triangulation of human pose"
    - "self supervised learning of 3d human pose using multi view geometry"
    - "unsupervised 3d pose estimation with geometric self supervision"
    - "vehicle re identification with viewpoint aware metric learning"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "unsupervised geometry aware representation for 3d human pose estimation"

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)