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: "differentiable convex optimization layers"
  Abstract: Recent work has shown how to embed differentiable optimization problems (that is, problems whose solutions can be backpropagated through) as layers within deep learning architectures. This method provides a useful inductive bias for certain problems, but existing software for differentiable optimization layers is rigid and difficult to apply to new settings. In this paper, we propose an approach to differentiating through disciplined convex programs, a subclass of convex optimization problems used by domain-specific languages (DSLs) for convex optimization. We introduce disciplined parametrized programming, a subset of disciplined convex programming, and we show that every disciplined parametrized program can be represented as the composition of an affine map from parameters to problem data, a solver, and an affine map from the solver's solution to a solution of the original problem (a new form we refer to as affine-solver-affine form). We then demonstrate how to efficiently differentiate through each of these components, allowing for end-to-end analytical differentiation through the entire convex program. We implement our methodology in version 1.1 of CVXPY, a popular Python-embedded DSL for convex optimization, and additionally implement differentiable layers for disciplined convex programs in PyTorch and TensorFlow 2.0. Our implementation significantly lowers the barrier to using convex optimization problems in differentiable programs. We present applications in linear machine learning models and in stochastic control, and we show that our layer is competitive (in execution time) compared to specialized differentiable solvers from past work.

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"
    - "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"
    - "principled deep neural network training through linear programming"

Connected through hyperedge HE-2 (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"
    - "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"
    - "learning structured text representations"

Connected through hyperedge HE-3 (induced by source paper "neural lyapunov model predictive control"):
  with members:
    - "deep value model predictive control"
    - "tustin neural networks a class of recurrent nets for adaptive mpc of mechanical systems"
    - "accelerating neural odes with spectral elements"
    - "when to trust your model model based policy optimization"
    - "pytorch an imperative style high performance deep learning library"
    - "differentiable mpc for end to end planning and control"
    - "lipschitz continuity in model based reinforcement learning"
    - "plan online learn offline efficient learning and exploration via model based control"

Connected through hyperedge HE-4 (induced by source paper "differentiable fixed point iteration layer"):
  with members:
    - "satnet bridging deep learning and logical reasoning using a differentiable satisfiability solver"
    - "anderson acceleration for geometry optimization and physics simulation"
    - "acquisition of localization confidence for accurate object detection"
    - "deep value networks learn to evaluate and iteratively refine structured outputs"
    - "optnet differentiable optimization as a layer in neural networks"
    - "input convex neural networks"
    - "structured prediction energy networks"
    - "flownet learning optical flow with convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "learning with differentiable perturbed optimizers"):
  with members:
    - "differentiation of blackbox combinatorial solvers"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "importance weighting without importance weights an efficient algorithm for combinatorial semi bandits"
    - "online linear optimization via smoothing"
    - "on measure concentration of random maximum a posteriori perturbations"
    - "on sampling from the gibbs distribution with random maximum a posteriori perturbations"
    - "on the partition function and random maximum a posteriori perturbations"
    - "optimization with sparsity inducing penalties"

Connected through hyperedge HE-6 (induced by source paper "differentiable combinatorial losses through generalized gradients of linear programs"):
  with members:
    - "mipaal mixed integer program as a layer"
    - "multi stage pretraining for abstractive summarization"
    - "improving sequence to sequence learning via optimal transport"
    - "d3tw discriminative differentiable dynamic time warping for weakly supervised action alignment and segmentation"
    - "melding the data decisions pipeline decision focused learning for combinatorial optimization"
    - "texar a modularized versatile and extensible toolkit for text generation"
    - "beyond gradient descent for regularized segmentation losses"
    - "optnet differentiable optimization as a layer in neural networks"

Connected through hyperedge HE-7 (induced by source paper "deep declarative networks a new hope"):
  with members:
    - "satnet bridging deep learning and logical reasoning using a differentiable satisfiability solver"
    - "deep equilibrium models"
    - "learning compact neural networks with regularization"
    - "neural ordinary differential equations"
    - "imposing hard constraints on deep networks promises and limitations"
    - "discriminatively learned hierarchical rank pooling networks"
    - "optnet differentiable optimization as a layer in neural networks"
    - "on differentiating parameterized argmin and argmax problems with application to bi level 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)