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: "agnostic learning of disjunctions on symmetric distributions"
  Abstract: We consider the problem of approximating and learning disjunctions (or equivalently, conjunctions) on symmetric distributions over $\{0,1\}^n$. Symmetric distributions are distributions whose PDF is invariant under any permutation of the variables. We give a simple proof that for every symmetric distribution $\mathcal{D}$, there exists a set of $n^{O(\log{(1/\epsilon)})}$ functions $\mathcal{S}$, such that for every disjunction $c$, there is function $p$, expressible as a linear combination of functions in $\mathcal{S}$, such that $p$ $\epsilon$-approximates $c$ in $\ell_1$ distance on $\mathcal{D}$ or $\mathbf{E}_{x \sim \mathcal{D}}[ |c(x)-p(x)|] \leq \epsilon$. This directly gives an agnostic learning algorithm for disjunctions on symmetric distributions that runs in time $n^{O( \log{(1/\epsilon)})}$. The best known previous bound is $n^{O(1/\epsilon^4)}$ and follows from approximation of the more general class of halfspaces (Wimmer, 2010). We also show that there exists a symmetric distribution $\mathcal{D}$, such that the minimum degree of a polynomial that $1/3$-approximates the disjunction of all $n$ variables is $\ell_1$ distance on $\mathcal{D}$ is $\Omega( \sqrt{n})$. Therefore the learning result above cannot be achieved via $\ell_1$-regression with a polynomial basis used in most other agnostic learning algorithms. #R##N#Our technique also gives a simple proof that for any product distribution $\mathcal{D}$ and every disjunction $c$, there exists a polynomial $p$ of degree $O(\log{(1/\epsilon)})$ such that $p$ $\epsilon$-approximates $c$ in $\ell_1$ distance on $\mathcal{D}$. This was first proved by Blais et al. (2008) via a more involved argument.

Connected through hyperedge HE-1 (induced by source paper "reliably learning the relu in polynomial time"):
  with members:
    - "the power of depth for feedforward neural networks"
    - "complexity theoretic limitations on learning halfspaces"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "breaking the curse of dimensionality with convex neural networks"
    - "bounded independence fools degree 2 threshold functions"
    - "noise tolerant learning the parity problem and the statistical query model"

Connected through hyperedge HE-2 (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"
    - "approximate resilience monotonicity and the complexity of agnostic learning"
    - "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-3 (induced by source paper "approximate resilience monotonicity and the complexity of agnostic learning"):
  with members:
    - "on the complexity of random satisfiability problems with planted solutions"
    - "the complexity of learning halfspaces using generalized linear methods"
    - "minimizing the misclassification error rate using a surrogate convex loss"
    - "submodular functions are noise stable"
    - "agnostic learning of monomials by halfspaces is hard"
    - "a complete characterization of statistical query learning with applications to evolvability"
    - "approximation resistant predicates from pairwise independence"

Connected through hyperedge HE-4 (induced by source paper "learning coverage functions and private release of marginals"):
  with members:
    - "faster private release of marginals on small databases"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "optimal bounds on approximation of submodular and xos functions by juntas"
    - "efficient algorithms for privately releasing marginals via convex relaxations"
    - "representation approximation and learning of submodular functions using low rank decision trees"
    - "fingerprinting codes and the price of approximate differential privacy"
    - "testing coverage functions"
    - "calibrating data to sensitivity in private data analysis"

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)