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: "nearly optimal solutions for the chow parameters problem and low weight approximation of halfspaces"
  Abstract: The \emph{Chow parameters} of a Boolean function $f: \{-1,1\}^n \to \{-1,1\}$ are its $n+1$ degree-0 and degree-1 Fourier coefficients. It has been known since 1961 (Chow, Tannenbaum) that the (exact values of the) Chow parameters of any linear threshold function $f$ uniquely specify $f$ within the space of all Boolean functions, but until recently (O'Donnell and Servedio) nothing was known about efficient algorithms for \emph{reconstructing} $f$ (exactly or approximately) from exact or approximate values of its Chow parameters. We refer to this reconstruction problem as the \emph{Chow Parameters Problem.} #R##N#Our main result is a new algorithm for the Chow Parameters Problem which, given (sufficiently accurate approximations to) the Chow parameters of any linear threshold function $f$, runs in time $\tilde{O}(n^2)\cdot (1/\eps)^{O(\log^2(1/\eps))}$ and with high probability outputs a representation of an LTF $f'$ that is $\eps$-close to $f$. The only previous algorithm (O'Donnell and Servedio) had running time $\poly(n) \cdot 2^{2^{\tilde{O}(1/\eps^2)}}.$ #R##N#As a byproduct of our approach, we show that for any linear threshold function $f$ over $\{-1,1\}^n$, there is a linear threshold function $f'$ which is $\eps$-close to $f$ and has all weights that are integers at most $\sqrt{n} \cdot (1/\eps)^{O(\log^2(1/\eps))}$. This significantly improves the best previous result of Diakonikolas and Servedio which gave a $\poly(n) \cdot 2^{\tilde{O}(1/\eps^{2/3})}$ weight bound, and is close to the known lower bound of $\max\{\sqrt{n},$ $(1/\eps)^{\Omega(\log \log (1/\eps))}\}$ (Goldberg, Servedio). Our techniques also yield improved algorithms for related problems in learning theory.

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 "kruskal katona for convex sets with applications"):
  with members:
    - "learning geometric concepts with nasty noise"
    - "beyond talagrand functions new lower bounds for testing monotonicity and unateness"
    - "a polynomial lower bound for testing monotonicity"
    - "analysis of boolean functions"
    - "boolean function monotonicity testing requires almost n 1 2 non adaptive queries"
    - "an invariance principle for polytopes"
    - "average sensitivity and noise sensitivity of polynomial threshold functions"

Connected through hyperedge HE-3 (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-4 (induced by source paper "on the complexity of the inverse semivalue problem for weighted voting games"):
  with members:
    - "degree d chow parameters robustly determine degree d ptfs and algorithmic applications"
    - "algorithmic bayesian persuasion"
    - "public projects boolean functions and the borders of border s theorem"
    - "a robust khintchine inequality and algorithms for computing optimal constants in fourier analysis and high dimensional geometry"
    - "the inverse shapley value problem"
    - "the complexity of power index comparison"
    - "complexity of comparison of influence of players in simple games"

Connected through hyperedge HE-5 (induced by source paper "learning geometric concepts with nasty noise"):
  with members:
    - "being robust in high dimensions can be practical"
    - "robustly learning a gaussian getting optimal error efficiently"
    - "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures"
    - "robust estimators in high dimensions without the computational intractability"
    - "complexity theoretic limitations on learning halfspaces"
    - "a ptas for agnostically learning halfspaces"
    - "the power of localization for efficiently learning linear separators with noise"
    - "the average sensitivity of an intersection of half spaces"

Connected through hyperedge HE-6 (induced by source paper "public projects boolean functions and the borders of border s theorem"):
  with members:
    - "analysis of boolean functions"
    - "the complexity of optimal multidimensional pricing"
    - "bayesian optimal auctions via multi to single agent reduction"
    - "optimal multi dimensional mechanism design reducing revenue to welfare maximization"
    - "the complexity of optimal mechanism design"
    - "a robust khintchine inequality and algorithms for computing optimal constants in fourier analysis and high dimensional geometry"
    - "an algorithmic characterization of multi dimensional mechanisms"
    - "optimal auctions with correlated bidders are easy"

Connected through hyperedge HE-7 (induced by source paper "ready for the design of voting rules"):
  with members:
    - "the inverse problem for power distributions in committees"
    - "the inverse shapley value problem"

Connected through hyperedge HE-8 (induced by source paper "the inverse problem for power distributions in committees"):
  with members:
    - "measuring voting power in convex policy spaces"
    - "the inverse shapley value problem"

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)