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: "structured sparsity inducing norms through submodular functions"
  Abstract: Sparse methods for supervised learning aim at finding good linear predictors from as few variables as possible, i.e., with small cardinality of their supports. This combinatorial selection problem is often turned into a convex optimization problem by replacing the cardinality function by its convex envelope (tightest convex lower bound), in this case the L1-norm. In this paper, we investigate more general set-functions than the cardinality, that may incorporate prior knowledge or structural constraints which are common in many applications: namely, we show that for nondecreasing submodular set-functions, the corresponding convex envelope can be obtained from its \lova extension, a common tool in submodular analysis. This defines a family of polyhedral norms, for which we provide generic algorithmic tools (subgradients and proximal operators) and theoretical results (conditions for support recovery or high-dimensional inference). By selecting specific submodular functions, we can give a new interpretation to known norms, such as those based on rank-statistics or grouped norms with potentially overlapping groups; we also define new norms, in particular ones that can be used as non-factorial priors for supervised learning.

Connected through hyperedge HE-1 (induced by source paper "safe screening for the generalized conditional gradient method"):
  with members:
    - "deep frank wolfe for neural network optimization"
    - "multi task learning as multi objective optimization"
    - "joint screening tests for lasso"
    - "tight bounds for approximate carath eodory and beyond"
    - "the ordered weighted ell_1 norm atomic formulation projections and algorithms"
    - "a safe screening rule for sparse logistic regression"
    - "safe screening with variational inequalities and its application to lasso"
    - "duality between subgradient and conditional gradient methods"

Connected through hyperedge HE-2 (induced by source paper "black box submodular maximization discrete and continuous settings"):
  with members:
    - "introduction to online convex optimization"
    - "online continuous submodular maximization from full information to bandit feedback"
    - "optimal dr submodular maximization and applications to provable mean field inference"
    - "black box adversarial attacks with limited queries and information"
    - "discrete adversarial attacks and submodular optimization with applications to text classification"
    - "weakly submodular maximization beyond cardinality constraints does randomization help greedy"
    - "gradient methods for submodular maximization"
    - "continuous dr submodular maximization structure and algorithms"

Connected through hyperedge HE-3 (induced by source paper "optimal approximation for unconstrained non submodular minimization"):
  with members:
    - "near optimal approximate discrete and continuous submodular function minimization"
    - "fast maximization of non submodular monotonic functions on the integer lattice"
    - "on maximization of weakly modular functions guarantees of multi stage algorithms tractability and hardness"
    - "gradient methods for submodular maximization"
    - "guarantees for greedy maximization of non submodular functions with applications"
    - "submodular optimization under noise"
    - "subquadratic submodular function minimization"
    - "tolerant junta testing and the connection to submodular optimization and function isomorphism"

Connected through hyperedge HE-4 (induced by source paper "differentially private empirical risk minimization with sparsity inducing norms"):
  with members:
    - "differentially private empirical risk minimization revisited faster and more general"
    - "efficient private erm for smooth objectives"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"
    - "private empirical risk minimization beyond the worst case the effect of the constraint set geometry"
    - "calibrating data to sensitivity in private data analysis"
    - "learning with submodular functions a convex optimization perspective"
    - "differentially private empirical risk minimization"

Connected through hyperedge HE-5 (induced by source paper "full characterization of optimal uncoded placement for the structured clique cover delivery of nonuniform demands"):
  with members:
    - "the exact rate memory tradeoff for caching with uncoded prefetching"
    - "the optimal memory rate trade off for the non uniform centralized caching problem with two files under uncoded placement"
    - "adaptive delivery in caching networks"
    - "optimization of heterogeneous coded caching"
    - "structural properties of uncoded placement optimization for coded delivery"
    - "multihop caching aided coded multicasting for the next generation of cellular networks"
    - "caching at the wireless edge design aspects challenges and future directions"
    - "big data caching for networking moving from cloud to edge"

Connected through hyperedge HE-6 (induced by source paper "distributed submodular minimization over networks a greedy column generation approach"):
  with members:
    - "distributed submodular maximization with limited information"
    - "reflection methods for user friendly submodular optimization"
    - "a supermodular optimization framework for leader selection under link noise in linear multi agent systems"
    - "learning with submodular functions a convex optimization perspective"
    - "efficient minimization of decomposable submodular functions"
    - "distributed abstract optimization via constraints consensus theory and applications"

Connected through hyperedge HE-7 (induced by source paper "an adaptive sublinear time block sparse fourier transform"):
  with members:
    - "a robust sparse fourier transform in the continuous setting"
    - "sparse fourier transform in any constant dimension with nearly optimal sample complexity in sublinear time"
    - "sample optimal average case sparse fourier transform in two dimensions"
    - "computing a k sparse n length discrete fourier transform using at most 4k samples and o k log k complexity"
    - "group sparse model selection hardness and relaxations"
    - "on model based rip 1 matrices"
    - "nearly optimal sparse fourier transform"
    - "1 eps approximate sparse recovery"

Connected through hyperedge HE-8 (induced by source paper "combinatorial penalties which structures are preserved by convex relaxations"):
  with members:
    - "gradient methods for submodular maximization"
    - "a totally unimodular view of structured sparsity"
    - "near optimal nonmyopic value of information in graphical models"
    - "optimization with sparsity inducing penalties"
    - "learning with submodular functions a convex optimization perspective"
    - "model based compressive sensing"
    - "submodular functions extensions distributions and algorithms a survey"

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)