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: "learning dnf expressions from fourier spectrum"
  Abstract: Since its introduction by Valiant in 1984, PAC learning of DNF expressions remains one of the central problems in learning theory. We consider this problem in the setting where the underlying distribution is uniform, or more generally, a product distribution. Kalai, Samorodnitsky and Teng (2009) showed that in this setting a DNF expression can be efficiently approximated from its "heavy" low-degree Fourier coefficients alone. This is in contrast to previous approaches where boosting was used and thus Fourier coefficients of the target function modified by various distributions were needed. This property is crucial for learning of DNF expressions over smoothed product distributions, a learning model introduced by Kalai et al. (2009) and inspired by the seminal smoothed analysis model of Spielman and Teng (2001). #R##N#We introduce a new approach to learning (or approximating) a polynomial threshold functions which is based on creating a function with range [-1,1] that approximately agrees with the unknown function on low-degree Fourier coefficients. We then describe conditions under which this is sufficient for learning polynomial threshold functions. Our approach yields a new, simple algorithm for approximating any polynomial-size DNF expression from its "heavy" low-degree Fourier coefficients alone. Our algorithm greatly simplifies the proof of learnability of DNF expressions over smoothed product distributions. We also describe an application of our algorithm to learning monotone DNF expressions over product distributions. Building on the work of Servedio (2001), we give an algorithm that runs in time $\poly((s \cdot \log{(s/\eps)})^{\log{(s/\eps)}}, n)$, where $s$ is the size of the target DNF expression and $\eps$ is the accuracy. This improves on $\poly((s \cdot \log{(ns/\eps)})^{\log{(s/\eps)} \cdot \log{(1/\eps)}}, n)$ bound of Servedio (2001).

Connected through hyperedge HE-1 (induced by source paper "hardness of learning dnfs using halfspaces"):
  with members:
    - "learning convex polytopes with margin"
    - "hardness of learning noisy halfspaces using polynomial thresholds"
    - "analysis of boolean functions"
    - "hardness results for agnostically learning low degree polynomial threshold functions"
    - "agnostic learning of monomials by halfspaces is hard"

Connected through hyperedge HE-2 (induced by source paper "a scheme for continuous input to the tsetlin machine with applications to forecasting disease outbreaks"):
  with members:
    - "using the tsetlin machine to learn human interpretable rules for high accuracy text categorization with medical applications"
    - "the tsetlin machine a game theoretic bandit driven approach to optimal pattern recognition with propositional logic"
    - "neural algebra of classifiers"

Connected through hyperedge HE-3 (induced by source paper "learning small predictors"):
  with members:
    - "thinet a filter level pruning method for deep neural network compression"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "compressing neural networks with the hashing trick"

Connected through hyperedge HE-4 (induced by source paper "boolean decision rules via column generation"):
  with members:
    - "scalable bayesian rule lists"
    - "learning optimized or s of and s"
    - "falling rule lists"

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 "learning depth three neural networks in polynomial time"):
  with members:
    - "learning relus via gradient descent"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "recovery guarantees for one hidden layer neural networks"
    - "convergence analysis of two layer neural networks with relu activation"
    - "toward deeper understanding of neural networks the power of initialization and a dual view on expressivity"
    - "complexity theoretic limitations on learning halfspaces"
    - "a ptas for agnostically learning halfspaces"

Connected through hyperedge HE-7 (induced by source paper "or s of and s for interpretable classification with application to context aware recommender systems"):
  with members:
    - "falling rule lists"

Connected through hyperedge HE-8 (induced by source paper "nearly optimal solutions for the chow parameters problem and low weight approximation of halfspaces"):
  with members:
    - "the inverse shapley value problem"
    - "agnostic learning of monomials by halfspaces is hard"
    - "improved approximation of linear threshold functions"
    - "bounded independence fools halfspaces"
    - "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)