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: "submodular functions are noise stable"
  Abstract: We show that all non-negative submodular functions have high {\em noise-stability}. As a consequence, we obtain a polynomial-time learning algorithm for this class with respect to any product distribution on $\{-1,1\}^n$ (for any constant accuracy parameter $\epsilon$). Our algorithm also succeeds in the agnostic setting. Previous work on learning submodular functions required either query access or strong assumptions about the types of submodular functions to be learned (and did not hold in the agnostic setting).

Connected through hyperedge HE-1 (induced by source paper "approximate mathbb f _2 sketching of valuation functions"):
  with members:
    - "optimality of linear sketching under modular updates"
    - "optimal lower bounds for universal relation and for samplers and finding duplicates in streams"
    - "testing submodularity and other properties of valuation functions"
    - "almost optimal streaming algorithms for coverage problems"
    - "tight bounds for single pass streaming complexity of the set cover problem"
    - "towards tight bounds for the streaming set cover problem"
    - "streaming algorithms for submodular function maximization"
    - "incidence geometries and the pass complexity of semi streaming set cover"

Connected through hyperedge HE-2 (induced by source paper "on the tractability of public persuasion with no externalities"):
  with members:
    - "algorithmic persuasion with no externalities"
    - "bayesian exploration incentivizing exploration in bayesian games"
    - "no regret learning in bayesian games"
    - "algorithmic bayesian persuasion"
    - "deterministic algorithms for submodular maximization problems"
    - "hardness results for signaling in bayesian zero sum and network routing games"
    - "honest signaling in zero sum games is hard and lying is even harder"
    - "mixture selection mechanism design and signaling"

Connected through hyperedge HE-3 (induced by source paper "approximating the noise sensitivity of a monotone boolean function"):
  with members:
    - "a polynomial lower bound for testing monotonicity"
    - "analysis of boolean functions"
    - "new algorithms and lower bounds for monotonicity testing"
    - "the average sensitivity of an intersection of half spaces"
    - "active property testing"
    - "average sensitivity and noise sensitivity of polynomial threshold functions"

Connected through hyperedge HE-4 (induced by source paper "spectral sparsification of hypergraphs"):
  with members:
    - "cheeger inequalities for submodular transformations"
    - "sketching cuts in graphs and hypergraphs"
    - "hypergraph markov operators eigenvalues and approximation algorithms"
    - "representation approximation and learning of submodular functions using low rank decision trees"
    - "learning pseudo boolean k dnf and submodular functions"
    - "fast approximation algorithms for cut based problems in undirected graphs"
    - "privately releasing conjunctions and the statistical query barrier"
    - "twice ramanujan sparsifiers"

Connected through hyperedge HE-5 (induced by source paper "learning neural networks with two nonlinear layers in polynomial time"):
  with members:
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "on the complexity of learning neural networks"
    - "learning relus via gradient descent"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "recovery guarantees for one hidden layer neural networks"
    - "electron proton dynamics in deep learning"
    - "sgd learns the conjugate kernel class of the network"
    - "convergence analysis of two layer neural networks with relu activation"

Connected through hyperedge HE-6 (induced by source paper "differential privacy on finite computers"):
  with members:
    - "priv it private and sample efficient identity testing"
    - "simultaneous private learning of multiple concepts"
    - "faster private release of marginals on small databases"
    - "efficient algorithms for privately releasing marginals via convex relaxations"
    - "preserving differential privacy under finite precision semantics"
    - "calibrating data to sensitivity in private data analysis"
    - "faster algorithms for privately releasing marginals"
    - "a learning theory approach to non interactive database privacy"

Connected through hyperedge HE-7 (induced by source paper "the limitations of optimization from samples"):
  with members:
    - "introduction to online convex optimization"
    - "online submodular welfare maximization greedy beats 1 2 in random order"
    - "submodular optimization under noise"
    - "robust influence maximization"
    - "the sample complexity of revenue maximization"
    - "the pseudo dimension of near optimal auctions"
    - "local distribution and the symmetry gap approximability of multiway partitioning problems"
    - "mixture selection mechanism design and signaling"

Connected through hyperedge HE-8 (induced by source paper "tight bounds on low degree spectral concentration of submodular and xos functions"):
  with members:
    - "analysis of boolean functions"
    - "approximate resilience monotonicity and the complexity of agnostic learning"
    - "nearly tight bounds on ell_1 approximation of self bounding functions"
    - "optimal bounds on approximation of submodular and xos functions by juntas"
    - "representation approximation and learning of submodular functions using low rank decision trees"
    - "learning coverage functions and private release of marginals"
    - "on the sum of l1 influences"
    - "learning pseudo boolean k dnf and submodular functions"

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)