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: "the complexity of learning halfspaces using generalized linear methods"
  Abstract: Many popular learning algorithms (E.g. Regression, Fourier-Transform based algorithms, Kernel SVM and Kernel ridge regression) operate by reducing the problem to a convex optimization problem over a vector space of functions. These methods offer the currently best approach to several central problems such as learning half spaces and learning DNF's. In addition they are widely used in numerous application domains. Despite their importance, there are still very few proof techniques to show limits on the power of these algorithms. #R##N#We study the performance of this approach in the problem of (agnostically and improperly) learning halfspaces with margin $\gamma$. Let $\mathcal{D}$ be a distribution over labeled examples. The $\gamma$-margin error of a hyperplane $h$ is the probability of an example to fall on the wrong side of $h$ or at a distance $\le\gamma$ from it. The $\gamma$-margin error of the best $h$ is denoted $\mathrm{Err}_\gamma(\mathcal{D})$. An $\alpha(\gamma)$-approximation algorithm receives $\gamma,\epsilon$ as input and, using i.i.d. samples of $\mathcal{D}$, outputs a classifier with error rate $\le \alpha(\gamma)\mathrm{Err}_\gamma(\mathcal{D}) + \epsilon$. Such an algorithm is efficient if it uses $\mathrm{poly}(\frac{1}{\gamma},\frac{1}{\epsilon})$ samples and runs in time polynomial in the sample size. #R##N#The best approximation ratio achievable by an efficient algorithm is $O\left(\frac{1/\gamma}{\sqrt{\log(1/\gamma)}}\right)$ and is achieved using an algorithm from the above class. Our main result shows that the approximation ratio of every efficient algorithm from this family must be $\ge \Omega\left(\frac{1/\gamma}{\mathrm{poly}\left(\log\left(1/\gamma\right)\right)}\right)$, essentially matching the best known upper bound.

Connected through hyperedge HE-1 (induced by source paper "time space tradeoffs for distinguishing distributions and applications to security of goldreich s prg"):
  with members:
    - "memory sample tradeoffs for linear regression with small error"
    - "communication and memory efficient testing of discrete distributions"
    - "detecting correlations with little memory and communication"
    - "extractor based time space lower bounds for learning"
    - "mixing complexity and its applications to neural networks"
    - "fast learning requires good memory a time space lower bound for parity learning"
    - "information theoretically secure databases"
    - "strongly refuting random csps below the spectral threshold"

Connected through hyperedge HE-2 (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-3 (induced by source paper "learning rules first classifiers"):
  with members:
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "conditional linear regression"
    - "complexity theoretic limitations on learning halfspaces"
    - "learning kernel based halfspaces with the zero one loss"
    - "a system for induction of oblique decision trees"

Connected through hyperedge HE-4 (induced by source paper "complexity theoretic limitations on learning halfspaces"):
  with members:
    - "how to refute a random csp"
    - "tensor prediction rademacher complexity and random 3 xor"
    - "complexity theoretic limitations on learning dnf s"
    - "a ptas for agnostically learning halfspaces"
    - "the power of localization for efficiently learning linear separators with noise"
    - "from average case complexity to improper learning complexity"
    - "more data speeds up training time in learning halfspaces over sparse vectors"
    - "on the complexity of random satisfiability problems with planted solutions"

Connected through hyperedge HE-5 (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"
    - "agnostic learning of disjunctions on symmetric distributions"
    - "approximate resilience monotonicity and the complexity of agnostic learning"
    - "pseudorandomness for regular branching programs via fourier analysis"
    - "fooling functions of halfspaces under product distributions"
    - "a derandomized sparse johnson lindenstrauss transform"
    - "bounded independence fools halfspaces"

Connected through hyperedge HE-6 (induced by source paper "a ptas for agnostically learning halfspaces"):
  with members:
    - "complexity theoretic limitations on learning halfspaces"
    - "complexity theoretic limitations on learning dnf s"
    - "the power of localization for efficiently learning linear separators with noise"
    - "from average case complexity to improper learning complexity"
    - "minimizing the misclassification error rate using a surrogate convex loss"
    - "bounded independence fools halfspaces"
    - "polynomial threshold functions structure approximation and pseudorandomness"
    - "bounded independence fools degree 2 threshold functions"

Connected through hyperedge HE-7 (induced by source paper "approximate resilience monotonicity and the complexity of agnostic learning"):
  with members:
    - "agnostic learning of disjunctions on symmetric distributions"
    - "on the complexity of random satisfiability problems with planted solutions"
    - "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-8 (induced by source paper "from average case complexity to improper learning complexity"):
  with members:
    - "more data speeds up training time in learning halfspaces over sparse vectors"
    - "approximation resistant predicates from pairwise independence"
    - "noise tolerant learning the parity problem and the statistical query model"

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)