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: "hardness results for agnostically learning low degree polynomial threshold functions"
  Abstract: Hardness results for maximum agreement problems have close connections to hardness results for proper learning in computational learning theory. In this paper we prove two hardness results for the problem of finding a low degree polynomial threshold function (PTF) which has the maximum possible agreement with a given set of labeled examples in $\R^n \times \{-1,1\}.$ We prove that for any constants $d\geq 1, \eps > 0$, #R##N#{itemize} #R##N#Assuming the Unique Games Conjecture, no polynomial-time algorithm can find a degree-$d$ PTF that is consistent with a $(\half + \eps)$ fraction of a given set of labeled examples in $\R^n \times \{-1,1\}$, even if there exists a degree-$d$ PTF that is consistent with a $1-\eps$ fraction of the examples. #R##N#It is $\NP$-hard to find a degree-2 PTF that is consistent with a $(\half + \eps)$ fraction of a given set of labeled examples in $\R^n \times \{-1,1\}$, even if there exists a halfspace (degree-1 PTF) that is consistent with a $1 - \eps$ fraction of the examples. #R##N#{itemize} #R##N#These results immediately imply the following hardness of learning results: (i) Assuming the Unique Games Conjecture, there is no better-than-trivial proper learning algorithm that agnostically learns degree-$d$ PTFs under arbitrary distributions; (ii) There is no better-than-trivial learning algorithm that outputs degree-2 PTFs and agnostically learns halfspaces (i.e. degree-1 PTFs) under arbitrary distributions.

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 "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"
    - "learning dnf expressions from fourier spectrum"
    - "agnostic learning of monomials by halfspaces is hard"

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 "luby veli v c kovi c wigderson revisited improved correlation bounds and pseudorandom generators for depth two circuits"):
  with members:
    - "improved pseudorandom generators from pseudorandom multi switching lemmas"
    - "pseudorandom generators for polynomial threshold functions"
    - "bounded independence fools degree 2 threshold functions"

Connected through hyperedge HE-5 (induced by source paper "deterministic approximate counting for degree 2 polynomial threshold functions"):
  with members:
    - "deterministic approximate counting for juntas of degree 2 polynomial threshold functions"
    - "efficient deterministic approximate counting for low degree polynomial threshold functions"
    - "a pseudorandom generator for polynomial threshold functions of gaussian with subpolynomial seed length"
    - "the inverse shapley value problem"
    - "nearly optimal solutions for the chow parameters problem and low weight approximation of halfspaces"
    - "correlation decay up to uniqueness in spin systems"
    - "a small prg for polynomial threshold functions of gaussians"
    - "agnostic learning of monomials by halfspaces is hard"

Connected through hyperedge HE-6 (induced by source paper "deterministic approximate counting for juntas of degree 2 polynomial threshold functions"):
  with members:
    - "deterministic approximate counting for degree 2 polynomial threshold functions"
    - "the inverse shapley value problem"
    - "nearly optimal solutions for the chow parameters problem and low weight approximation of halfspaces"
    - "fooling functions of halfspaces under product distributions"
    - "k independent gaussians fool polynomial threshold functions"
    - "bounded independence fools halfspaces"
    - "polynomial threshold functions structure approximation and pseudorandomness"
    - "bounding the sensitivity of polynomial threshold functions"

Connected through hyperedge HE-7 (induced by source paper "efficient deterministic approximate counting for low degree polynomial threshold functions"):
  with members:
    - "deterministic approximate counting for juntas of degree 2 polynomial threshold functions"
    - "deterministic approximate counting for degree 2 polynomial threshold functions"
    - "a pseudorandom generator for polynomial threshold functions of gaussian with subpolynomial seed length"
    - "the inverse shapley value problem"
    - "nearly optimal solutions for the chow parameters problem and low weight approximation of halfspaces"
    - "a small prg for polynomial threshold functions of gaussians"
    - "fooling functions of halfspaces under product distributions"
    - "k independent gaussians fool polynomial threshold functions"

Connected through hyperedge HE-8 (induced by source paper "moment matching polynomials"):
  with members:
    - "agnostic learning of monomials by halfspaces is hard"
    - "bounding the sensitivity of polynomial threshold functions"
    - "a regularity lemma and low weight approximators for low degree polynomial threshold functions"
    - "pseudorandom generators for polynomial threshold functions"
    - "bounded independence fools degree 2 threshold functions"
    - "smoothed analysis of algorithms why the simplex algorithm usually takes polynomial time"

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)