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: "on differentiating parameterized argmin and argmax problems with application to bi level optimization"
  Abstract: Some recent works in machine learning and computer vision involve the solution of a bi-level optimization problem. Here the solution of a parameterized lower-level problem binds variables that appear in the objective of an upper-level problem. The lower-level problem typically appears as an argmin or argmax optimization problem. Many techniques have been proposed to solve bi-level optimization problems, including gradient descent, which is popular with current end-to-end learning approaches. In this technical report we collect some results on differentiating argmin and argmax optimization problems with and without constraints and provide some insightful motivating examples.

Connected through hyperedge HE-1 (induced by source paper "spatio temporal ranked attention networks for video captioning"):
  with members:
    - "spatio temporal dynamics and semantic attribute enriched visual encoding for video captioning"
    - "object aware aggregation with bidirectional temporal graph for video captioning"
    - "less is more picking informative frames for video captioning"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "watch listen and describe globally and locally aligned cross modal attentions for video captioning"
    - "the sound of pixels"
    - "improved image captioning via policy gradient optimization of spider"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-2 (induced by source paper "joint unsupervised learning of optical flow and egomotion with bi level optimization"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "unsupervised deep epipolar flow for stationary or dynamic scenes"
    - "unsupervised scale consistent depth and ego motion learning from monocular video"
    - "depth from videos in the wild unsupervised monocular depth learning from unknown cameras"
    - "deep declarative networks a new hope"
    - "meta learning with implicit gradients"
    - "ddflow learning optical flow with unlabeled data distillation"
    - "selflow self supervised learning of optical flow"

Connected through hyperedge HE-3 (induced by source paper "lp sparsemap differentiable relaxed optimization for sparse structured prediction"):
  with members:
    - "learning latent trees with stochastic perturbations and differentiable dynamic programming"
    - "sparse sequence to sequence models"
    - "differentiable convex optimization layers"
    - "meta learning with implicit gradients"
    - "listops a diagnostic dataset for latent tree learning"
    - "backpropagating through structured argmax using a spigot"
    - "differentiable perturb and parse semi supervised parsing with a structured variational autoencoder"
    - "structured attention networks"

Connected through hyperedge HE-4 (induced by source paper "implicitly defined layers in neural networks"):
  with members:
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "matryoshka networks predicting 3d geometry via nested shape layers"
    - "optnet differentiable optimization as a layer in neural networks"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "learning a predictable and generative vector representation for objects"
    - "a point set generation network for 3d object reconstruction from a single image"
    - "unsupervised learning of 3d structure from images"
    - "warpnet weakly supervised matching for single view reconstruction"

Connected through hyperedge HE-5 (induced by source paper "the differentiable cross entropy method"):
  with members:
    - "variational inference mpc for bayesian model based reinforcement learning"
    - "exploring model based planning with policy networks"
    - "the limited multi label projection layer"
    - "bayesian optimization in variational latent spaces with dynamic compression"
    - "unsupervised state representation learning in atari"
    - "adaptive and safe bayesian optimization in high dimensions via one dimensional subspaces"
    - "learning latent plans from play"
    - "learning action representations for reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "discriminative video representation learning using support vector classifiers"):
  with members:
    - "moments in time dataset one million videos for event understanding"
    - "a short note about kinetics 600"
    - "non linear temporal subspace representations for activity recognition"
    - "video representation learning using discriminative pooling"
    - "higher order pooling of cnn features via kernel linearization for action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "generalized rank pooling for activity recognition"
    - "appearance and relation networks for video classification"

Connected through hyperedge HE-7 (induced by source paper "end to end learnable geometric vision by backpropagating pnp optimization"):
  with members:
    - "neural guided ransac learning where to sample model hypotheses"
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "satellite pose estimation with deep landmark regression and nonlinear pose refinement"
    - "expert sample consensus applied to camera re localization"
    - "deep high resolution representation learning for human pose estimation"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "numerical coordinate regression with convolutional neural networks"
    - "geometric loss functions for camera pose regression with deep learning"

Connected through hyperedge HE-8 (induced by source paper "adaptively sparse transformers"):
  with members:
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "ssn learning sparse switchable normalization via sparsestmax"
    - "bert rediscovers the classical nlp pipeline"
    - "pay less attention with lightweight and dynamic convolutions"
    - "sparse sequence to sequence models"
    - "attention is not explanation"
    - "generating long sequences with sparse transformers"
    - "selective attention for context aware neural machine translation"

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)