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: "a light touch for heavily constrained sgd"
  Abstract: Minimizing empirical risk subject to a set of constraints can be a useful strategy for learning restricted classes of functions, such as monotonic functions, submodular functions, classifiers that guarantee a certain class label for some subset of examples, etc. However, these restrictions may result in a very large number of constraints. Projected stochastic gradient descent (SGD) is often the default choice for large-scale optimization in machine learning, but requires a projection after each update. For heavily-constrained objectives, we propose an efficient extension of SGD that stays close to the feasible region while only applying constraints probabilistically at each iteration. Theoretical analysis shows a compelling trade-off between per-iteration work and the number of iterations needed on problems with a large number of constraints.

Connected through hyperedge HE-1 (induced by source paper "optimization with non differentiable constraints with applications to fairness recall churn and other goals"):
  with members:
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"
    - "robust optimization for non convex objectives"
    - "learning non discriminatory predictors"
    - "equality of opportunity in supervised learning"
    - "label free supervision of neural networks with physics and domain knowledge"
    - "satisfying real world goals with dataset constraints"
    - "optimization learning and games with predictable sequences"
    - "alternating projections for learning with expectation constraints"

Connected through hyperedge HE-2 (induced by source paper "interpretable set functions"):
  with members:
    - "deep sets"
    - "monotonic calibrated interpolated look up tables"
    - "adam a method for stochastic optimization"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-3 (induced by source paper "monotonic calibrated interpolated look up tables"):
  with members:
    - "learning with submodular functions a convex optimization perspective"
    - "optimal distributed online prediction using mini batches"
    - "inducing interpretable voting classifiers without trading accuracy for simplicity theoretical results approximation algorithms and experiments"

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)