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: "improved approximation of linear threshold functions"
  Abstract: We prove two main results on how arbitrary linear threshold functions $f(x) = \sign(w\cdot x - \theta)$ over the $n$-dimensional Boolean hypercube can be approximated by simple threshold functions. #R##N#Our first result shows that every $n$-variable threshold function $f$ is $\eps$-close to a threshold function depending only on $\Inf(f)^2 \cdot \poly(1/\eps)$ many variables, where $\Inf(f)$ denotes the total influence or average sensitivity of $f.$ This is an exponential sharpening of Friedgut's well-known theorem \cite{Friedgut:98}, which states that every Boolean function $f$ is $\eps$-close to a function depending only on $2^{O(\Inf(f)/\eps)}$ many variables, for the case of threshold functions. We complement this upper bound by showing that $\Omega(\Inf(f)^2 + 1/\epsilon^2)$ many variables are required for $\epsilon$-approximating threshold functions. #R##N#Our second result is a proof that every $n$-variable threshold function is $\eps$-close to a threshold function with integer weights at most $\poly(n) \cdot 2^{\tilde{O}(1/\eps^{2/3})}.$ This is a significant improvement, in the dependence on the error parameter $\eps$, on an earlier result of \cite{Servedio:07cc} which gave a $\poly(n) \cdot 2^{\tilde{O}(1/\eps^{2})}$ bound. Our improvement is obtained via a new proof technique that uses strong anti-concentration bounds from probability theory. The new technique also gives a simple and modular proof of the original \cite{Servedio:07cc} result, and extends to give low-weight approximators for threshold functions under a range of probability distributions beyond just the uniform distribution.

Connected through hyperedge HE-1 (induced by source paper "nearly tight bounds for robust proper learning of halfspaces with a margin"):
  with members:
    - "adversarial robustness may be at odds with simplicity"
    - "vc classes are adversarially robustly learnable but only improperly"
    - "efficient algorithms and lower bounds for robust linear regression"
    - "degree d chow parameters robustly determine degree d ptfs and algorithmic applications"
    - "efficient algorithms for outlier robust regression"
    - "sever a robust meta algorithm for stochastic optimization"
    - "learning geometric concepts with nasty noise"
    - "being robust in high dimensions can be practical"

Connected through hyperedge HE-2 (induced by source paper "degree d chow parameters robustly determine degree d ptfs and algorithmic applications"):
  with members:
    - "learning geometric concepts with nasty noise"
    - "hardness of learning noisy halfspaces using polynomial thresholds"
    - "robust estimators in high dimensions without the computational intractability"
    - "pseudorandomness via the discrete fourier transform"
    - "complexity theoretic limitations on learning halfspaces"
    - "super linear gate and super quadratic wire lower bounds for depth two and depth three threshold circuits"
    - "a ptas for agnostically learning halfspaces"
    - "analysis of boolean functions"

Connected through hyperedge HE-3 (induced by source paper "fooling intersections of low weight halfspaces"):
  with members:
    - "average case lower bounds and satisfiability algorithms for small threshold circuits"
    - "pseudorandomness via the discrete fourier transform"
    - "analysis of boolean functions"
    - "the average sensitivity of an intersection of half spaces"
    - "deterministic approximate counting for juntas of degree 2 polynomial threshold functions"
    - "efficient deterministic approximate counting for low degree polynomial threshold functions"
    - "an invariance principle for polytopes"
    - "a robust khintchine inequality and algorithms for computing optimal constants in fourier analysis and high dimensional geometry"

Connected through hyperedge HE-4 (induced by source paper "a polynomial lower bound for testing monotonicity"):
  with members:
    - "boolean function monotonicity testing requires almost n 1 2 non adaptive queries"
    - "new algorithms and lower bounds for monotonicity testing"
    - "an optimal lower bound for monotonicity testing over hypergrids"
    - "a o n monotonicity tester for boolean functions over the hypercube"

Connected through hyperedge HE-5 (induced by source paper "efficiently learning ising models on arbitrary graphs"):
  with members:
    - "hardness of parameter estimation in graphical models"
    - "analysis of boolean functions"
    - "maximum likelihood bounded tree width markov networks"
    - "moment matching polynomials"
    - "approximation algorithms for two state anti ferromagnetic spin systems on bounded degree graphs"
    - "on learning discrete graphical models using greedy methods"
    - "settling the polynomial learnability of mixtures of gaussians"
    - "computational transition at the uniqueness threshold"

Connected through hyperedge HE-6 (induced by source paper "analysis of boolean functions"):
  with members:
    - "improved bounds for the randomized decision tree complexity of recursive majority"
    - "some improvements to total degree tests"
    - "dnf sparsification and a faster deterministic counting algorithm"
    - "hypercontractive inequalities via sos with an application to vertex cover"
    - "hypercontractivity sum of squares proofs and their applications"
    - "majority is stablest discrete and sos"
    - "a satisfiability algorithm for ac 0"
    - "the influence lower bound via query elimination"

Connected through hyperedge HE-7 (induced by source paper "optimal bounds on approximation of submodular and xos functions by juntas"):
  with members:
    - "representation approximation and learning of submodular functions using low rank decision trees"
    - "learning coverage functions and private release of marginals"
    - "dnf sparsification and a faster deterministic counting algorithm"
    - "learning pseudo boolean k dnf and submodular functions"
    - "submodular functions are noise stable"
    - "learning valuation functions"
    - "a note on concentration of submodular functions"
    - "submodular functions learnability structure and optimization"

Connected through hyperedge HE-8 (induced by source paper "a polynomial time approximation scheme for fault tolerant distributed storage"):
  with members:
    - "stochastic combinatorial optimization via poisson approximation"
    - "nearly optimal solutions for the chow parameters problem and low weight approximation of halfspaces"
    - "maximizing expected utility for stochastic combinatorial optimization problems"
    - "symmetric allocations for distributed storage"
    - "memory allocation in distributed storage networks"
    - "bounded independence fools halfspaces"
    - "pseudorandom generators for polynomial threshold 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.CC(Computational Complexity)