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: "approximate resilience monotonicity and the complexity of agnostic learning"
  Abstract: A function $f$ is $d$-resilient if all its Fourier coefficients of degree at most $d$ are zero, i.e., $f$ is uncorrelated with all low-degree parities. We study the notion of $\mathit{approximate}$ $\mathit{resilience}$ of Boolean functions, where we say that $f$ is $\alpha$-approximately $d$-resilient if $f$ is $\alpha$-close to a $[-1,1]$-valued $d$-resilient function in $\ell_1$ distance. We show that approximate resilience essentially characterizes the complexity of agnostic learning of a concept class $C$ over the uniform distribution. Roughly speaking, if all functions in a class $C$ are far from being $d$-resilient then $C$ can be learned agnostically in time $n^{O(d)}$ and conversely, if $C$ contains a function close to being $d$-resilient then agnostic learning of $C$ in the statistical query (SQ) framework of Kearns has complexity of at least $n^{\Omega(d)}$. This characterization is based on the duality between $\ell_1$ approximation by degree-$d$ polynomials and approximate $d$-resilience that we establish. In particular, it implies that $\ell_1$ approximation by low-degree polynomials, known to be sufficient for agnostic learning over product distributions, is in fact necessary. #R##N#Focusing on monotone Boolean functions, we exhibit the existence of near-optimal $\alpha$-approximately $\widetilde{\Omega}(\alpha\sqrt{n})$-resilient monotone functions for all $\alpha>0$. Prior to our work, it was conceivable even that every monotone function is $\Omega(1)$-far from any $1$-resilient function. Furthermore, we construct simple, explicit monotone functions based on ${\sf Tribes}$ and ${\sf CycleRun}$ that are close to highly resilient functions. Our constructions are based on a fairly general resilience analysis and amplification. These structural results, together with the characterization, imply nearly optimal lower bounds for agnostic learning of monotone juntas.

Connected through hyperedge HE-1 (induced by source paper "weight sharing is crucial to succesful optimization"):
  with members:
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "failures of gradient based deep learning"
    - "sgd learns the conjugate kernel class of the network"
    - "distribution specific hardness of learning neural networks"
    - "identity matters in deep learning"
    - "ell_1 regularized neural networks are improperly learnable in polynomial time"
    - "global optimality in tensor factorization deep learning and beyond"

Connected through hyperedge HE-2 (induced by source paper "a general characterization of the statistical query complexity"):
  with members:
    - "on the power of learning from k wise queries"
    - "statistical query algorithms for mean vector estimation and stochastic convex optimization"
    - "generalization in adaptive data analysis and holdout reuse"
    - "algorithmic stability for adaptive data analysis"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "preserving statistical validity in adaptive data analysis"
    - "statistical active learning algorithms for noise tolerance and differential privacy"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-3 (induced by source paper "tight bounds on low degree spectral concentration of submodular and xos functions"):
  with members:
    - "analysis of boolean functions"
    - "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"
    - "submodular functions are noise stable"

Connected through hyperedge HE-4 (induced by source paper "weighted polynomial approximations limits for learning and pseudorandomness"):
  with members:
    - "pseudorandomness for concentration bounds and signed majorities"
    - "analysis of boolean functions"
    - "agnostic learning of disjunctions on symmetric distributions"
    - "pseudorandomness for regular branching programs via fourier analysis"
    - "the complexity of learning halfspaces using generalized linear methods"
    - "fooling functions of halfspaces under product distributions"
    - "a derandomized sparse johnson lindenstrauss transform"
    - "bounded independence fools halfspaces"

Connected through hyperedge HE-5 (induced by source paper "nearly tight bounds on ell_1 approximation of self bounding functions"):
  with members:
    - "analysis of boolean 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"
    - "learning pseudo boolean k dnf and submodular functions"
    - "submodular functions are noise stable"
    - "learning valuation functions"
    - "a note on concentration of submodular functions"

Connected through hyperedge HE-6 (induced by source paper "tight bounds on ell_1 approximation and learning of self bounding functions"):
  with members:
    - "tight bounds on low degree spectral concentration of submodular and xos functions"
    - "analysis of boolean functions"
    - "representation approximation and learning of submodular functions using low rank decision trees"
    - "learning coverage functions and private release of marginals"
    - "learning pseudo boolean k dnf and submodular functions"
    - "submodular functions are noise stable"
    - "learning valuation functions"
    - "submodular functions learnability structure and optimization"

Connected through hyperedge HE-7 (induced by source paper "agnostic learning of disjunctions on symmetric distributions"):
  with members:
    - "representation approximation and learning of submodular functions using low rank decision trees"
    - "learning coverage functions and private release of marginals"
    - "agnostic learning of monomials by halfspaces is hard"
    - "a complete characterization of statistical query learning with applications to evolvability"
    - "privately releasing conjunctions and the statistical query barrier"
    - "distribution specific agnostic boosting"

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)