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: "from average case complexity to improper learning complexity"
  Abstract: The basic problem in the PAC model of computational learning theory is to determine which hypothesis classes are efficiently learnable. There is presently a dearth of results showing hardness of learning problems. Moreover, the existing lower bounds fall short of the best known algorithms. #R##N#The biggest challenge in proving complexity results is to establish hardness of {\em improper learning} (a.k.a. representation independent learning).The difficulty in proving lower bounds for improper learning is that the standard reductions from $\mathbf{NP}$-hard problems do not seem to apply in this context. There is essentially only one known approach to proving lower bounds on improper learning. It was initiated in (Kearns and Valiant 89) and relies on cryptographic assumptions. #R##N#We introduce a new technique for proving hardness of improper learning, based on reductions from problems that are hard on average. We put forward a (fairly strong) generalization of Feige's assumption (Feige 02) about the complexity of refuting random constraint satisfaction problems. Combining this assumption with our new technique yields far reaching implications. In particular, #R##N#1. Learning $\mathrm{DNF}$'s is hard. #R##N#2. Agnostically learning halfspaces with a constant approximation ratio is hard. #R##N#3. Learning an intersection of $\omega(1)$ halfspaces is hard.

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 "equal opportunity in online classification with partial feedback"):
  with members:
    - "two player games for efficient non convex constrained optimization"
    - "a reductions approach to fair classification"
    - "calibrated fairness in bandits"
    - "runaway feedback loops in predictive policing"
    - "fairness in learning classic and contextual bandits"
    - "equality of opportunity in supervised learning"
    - "inherent trade offs in the fair determination of risk scores"
    - "taming the monster a fast and simple algorithm for contextual bandits"

Connected through hyperedge HE-3 (induced by source paper "constructing concrete hard instances of the maximum independent set problem"):
  with members:
    - "the backtracking survey propagation algorithm for solving random k sat problems"
    - "on independent sets in random graphs"
    - "average case complexity"
    - "generating hard satisfiable formulas by hiding solutions deceptively"

Connected through hyperedge HE-4 (induced by source paper "electron proton dynamics in deep learning"):
  with members:
    - "learning one hidden layer neural networks with landscape design"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "sgd learns the conjugate kernel class of the network"
    - "an analytical formula of population gradient for two layered relu network and its applications in convergence and critical point analysis"
    - "how to escape saddle points efficiently"
    - "convergence analysis of two layer neural networks with relu activation"
    - "deep residual networks with exponential linear unit"

Connected through hyperedge HE-5 (induced by source paper "sum of squares lower bounds for refuting any csp"):
  with members:
    - "lower bounds for csp refutation by sdp hierarchies"
    - "the backtracking survey propagation algorithm for solving random k sat problems"
    - "strongly refuting random csps below the spectral threshold"
    - "how to refute a random csp"
    - "noisy tensor completion via the sum of squares hierarchy"
    - "complexity theoretic limitations on learning halfspaces"
    - "sum of squares lower bounds from pairwise independence"
    - "lower bounds on the size of semidefinite programming relaxations"

Connected through hyperedge HE-6 (induced by source paper "globally optimal gradient descent for a convnet with gaussian inputs"):
  with members:
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "electron proton dynamics in deep learning"
    - "reliably learning the relu in polynomial time"
    - "understanding deep learning requires rethinking generalization"
    - "distribution specific hardness of learning neural networks"
    - "matrix completion has no spurious local minimum"
    - "gradient descent learns linear dynamical systems"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"

Connected through hyperedge HE-7 (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-8 (induced by source paper "sgd learns the conjugate kernel class of the network"):
  with members:
    - "random features for compositional kernels"
    - "toward deeper understanding of neural networks the power of initialization and a dual view on expressivity"
    - "provable learning of noisy or networks"
    - "convexified convolutional neural networks"
    - "learning halfspaces and neural networks with random initialization"
    - "deep convolutional networks are hierarchical kernel machines"
    - "on the equivalence between kernel quadrature rules and random feature expansions"
    - "ell_1 regularized neural networks are improperly learnable in 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)