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: "sparse projections onto the simplex"
  Abstract: Most learning methods with rank or sparsity constraints use convex relaxations, which lead to optimization with the nuclear norm or the $\ell_1$-norm. However, several important learning applications cannot benefit from this approach as they feature these convex norms as constraints in addition to the non-convex rank and sparsity constraints. In this setting, we derive efficient sparse projections onto the simplex and its extension, and illustrate how to use them to solve high-dimensional learning problems in quantum tomography, sparse density estimation and portfolio selection with non-convex constraints.

Connected through hyperedge HE-1 (induced by source paper "darc differentiable architecture compression"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "discrimination aware channel pruning for deep neural networks"
    - "darts differentiable architecture search"
    - "neural architecture search with bayesian optimisation and optimal transport"
    - "efficient neural architecture search via parameter sharing"
    - "progressive neural architecture search"
    - "shift a zero flop zero parameter alternative to spatial convolutions"

Connected through hyperedge HE-2 (induced by source paper "an empirical bayes approach to recovering linearly constrained non negative sparse signals"):
  with members:
    - "bilinear generalized approximate message passing part i derivation"
    - "on the convergence of approximate message passing with arbitrary matrices"
    - "fixed points of generalized approximate message passing with arbitrary matrices"
    - "expectation maximization gaussian mixture approximate message passing"
    - "approximate message passing with consistent parameter estimation and applications to sparse learning"
    - "the projected gsure for automatic parameter tuning in iterative shrinkage methods"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "optimally tuned iterative reconstruction algorithms for compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "joint inference of multiple label types in large networks"):
  with members:
    - "scaling graph based semi supervised learning to large number of labels using count min sketch"
    - "discriminative probabilistic models for relational data"
    - "an infinite latent attribute model for network data"

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)