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: "optnet differentiable optimization as a layer in neural networks"
  Abstract: This paper presents OptNet, a network architecture that integrates optimization problems (here, specifically in the form of quadratic programs) as individual layers in larger end-to-end trainable deep networks. These layers encode constraints and complex dependencies between the hidden states that traditional convolutional and fully-connected layers often cannot capture. In this paper, we explore the foundations for such an architecture: we show how techniques from sensitivity analysis, bilevel optimization, and implicit differentiation can be used to exactly differentiate through these layers and with respect to layer parameters; we develop a highly efficient solver for these layers that exploits fast GPU-based batch solves within a primal-dual interior point method, and which provides backpropagation gradients with virtually no additional cost on top of the solve; and we highlight the application of these approaches in several problems. In one notable example, we show that the method is capable of learning to play mini-Sudoku (4x4) given just input and output games, with no a priori information about the rules of the game; this highlights the ability of our architecture to learn hard constraints better than other neural architectures.

Connected through hyperedge HE-1 (induced by source paper "lossless compression of deep neural networks"):
  with members:
    - "mipaal mixed integer program as a layer"
    - "caql continuous action q learning"
    - "function approximation by deep networks"
    - "equivalent and approximate transformations of deep neural networks"
    - "differentiable convex optimization layers"
    - "accelerating primal solution findings for mixed integer programs based on solution prediction"
    - "reinforcement learning for integer programming learning to cut"
    - "machine learning for combinatorial optimization a methodological tour d horizon"

Connected through hyperedge HE-2 (induced by source paper "deepemd few shot image classification with differentiable earth mover s distance and structured classifiers"):
  with members:
    - "dense classification and implanting for few shot learning"
    - "few shot learning via saliency guided hallucination of samples"
    - "meta learning with differentiable convex optimization"
    - "few shot learning with localization in realistic settings"
    - "learning from adversarial features for few shot classification"
    - "revisiting local descriptor based image to class measure for few shot learning"
    - "meta learning with warped gradient descent"
    - "edge labeling graph neural network for few shot learning"

Connected through hyperedge HE-3 (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-4 (induced by source paper "differentiable top k operator with optimal transport"):
  with members:
    - "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"
    - "reparameterizable subset sampling via continuous relaxations"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "fine grained video categorization with redundancy reduction attention"
    - "neural nearest neighbors networks"
    - "attention gated networks learning to leverage salient regions in medical images"
    - "a continuous relaxation of beam search for end to end training of neural sequence models"
    - "sequence to sequence learning as beam search optimization"

Connected through hyperedge HE-5 (induced by source paper "automatic differentiation and continuous sensitivity analysis of rigid body dynamics"):
  with members:
    - "densephysnet learning dense physical object representations via multi step dynamic interactions"
    - "deep lagrangian networks using physics as model prior for deep learning"
    - "hamiltonian neural networks"
    - "deepmind control suite"
    - "a comparison of automatic differentiation and continuous sensitivity analysis for derivatives of differential equation solutions"
    - "learning particle dynamics for manipulating rigid bodies deformable objects and fluids"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "flexible neural representation for physics prediction"

Connected through hyperedge HE-6 (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-7 (induced by source paper "deep differentiable grasp planner for high dof grippers"):
  with members:
    - "generating grasp poses for a high dof gripper using neural networks"
    - "new formulation of mixed integer conic programming for globally optimal grasp planning"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "difftaichi differentiable programming for physical simulation"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "dexterous manipulation with deep reinforcement learning efficient general and low cost"
    - "neural kinematic networks for unsupervised motion retargetting"
    - "chainqueen a real time differentiable physical simulator for soft robotics"

Connected through hyperedge HE-8 (induced by source paper "a structured prediction approach for conditional meta learning"):
  with members:
    - "meta learning with implicit gradients"
    - "on first order meta learning algorithms"
    - "meta learning with latent embedding optimization"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "meta learning with differentiable closed form solvers"
    - "prototypical networks for few shot learning"
    - "few shot image recognition by predicting parameters from activations"
    - "meta sgd learning to learn quickly for few shot learning"

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)