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: "more data speeds up training time in learning halfspaces over sparse vectors"
  Abstract: The increased availability of data in recent years has led several authors to ask whether it is possible to use data as a {\em computational} resource. That is, if more data is available, beyond the sample complexity limit, is it possible to use the extra examples to speed up the computation time required to perform the learning task? #R##N#We give the first positive answer to this question for a {\em natural supervised learning problem} --- we consider agnostic PAC learning of halfspaces over $3$-sparse vectors in $\{-1,1,0\}^n$. This class is inefficiently learnable using $O\left(n/\epsilon^2\right)$ examples. Our main contribution is a novel, non-cryptographic, methodology for establishing computational-statistical gaps, which allows us to show that, under a widely believed assumption that refuting random $\mathrm{3CNF}$ formulas is hard, it is impossible to efficiently learn this class using only $O\left(n/\epsilon^2\right)$ examples. We further show that under stronger hardness assumptions, even $O\left(n^{1.499}/\epsilon^2\right)$ examples do not suffice. On the other hand, we show a new algorithm that learns this class efficiently using $\tilde{\Omega}\left(n^2/\epsilon^2\right)$ examples. This formally establishes the tradeoff between sample and computational complexity for a natural supervised learning problem.

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 "extended formulation lower bounds for refuting random csps"):
  with members:
    - "sum of squares lower bounds for refuting any csp"
    - "a nearly tight sum of squares lower bound for the planted clique problem"
    - "approximating rectangles by juntas and weakly exponential lower bounds for lp relaxations of csps"
    - "strongly refuting random csps below the spectral threshold"
    - "how to refute a random csp"
    - "complexity theoretic limitations on learning halfspaces"
    - "tensor prediction rademacher complexity and random 3 xor"
    - "lower bounds on the size of semidefinite programming relaxations"

Connected through hyperedge HE-3 (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-4 (induced by source paper "near optimal sample complexity for convex tensor completion"):
  with members:
    - "matrix completion via max norm constrained optimization"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "exact tensor completion using t svd"
    - "tensor prediction rademacher complexity and random 3 xor"
    - "nuclear norm of higher order tensors"
    - "rank regularization and bayesian inference for tensor completion and extrapolation"
    - "an alternating direction algorithm for matrix completion with nonnegative factors"
    - "concentration based guarantees for low rank matrix reconstruction"

Connected through hyperedge HE-5 (induced by source paper "tradeoffs between convergence speed and reconstruction accuracy in inverse problems"):
  with members:
    - "learning to learn by gradient descent by gradient descent"
    - "maximal sparsity with deep networks"
    - "onsager corrected deep learning for sparse linear inverse problems"
    - "accelerating eulerian fluid simulation with convolutional networks"
    - "sampling stationary signals subject to bitrate constraints"
    - "raisr rapid and accurate image super resolution"
    - "fundamental distortion limits of analog to digital compression"
    - "a picture is worth a billion bits real time image reconstruction from dense binary pixels"

Connected through hyperedge HE-6 (induced by source paper "strongly refuting random csps below the spectral threshold"):
  with members:
    - "certifying random polynomials over the unit sphere via sum of squares hierarchy"
    - "how to refute a random csp"
    - "complexity theoretic limitations on learning halfspaces"
    - "tensor principal component analysis via sum of squares proofs"
    - "tensor prediction rademacher complexity and random 3 xor"
    - "fast spectral algorithms from sum of squares proofs tensor decomposition and planted sparse vectors"
    - "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates"
    - "dictionary learning and tensor decomposition via the sum of squares method"

Connected through hyperedge HE-7 (induced by source paper "noisy tensor completion via the sum of squares hierarchy"):
  with members:
    - "speeding up sum of squares for tensor decomposition and planted sparse vectors"
    - "decomposing overcomplete 3rd order tensors using sum of squares algorithms"
    - "dictionary learning and tensor decomposition via the sum of squares method"
    - "sum of squares proofs and the quest toward optimal algorithms"
    - "rounding sum of squares relaxations"
    - "understanding alternating minimization for matrix completion"
    - "learning mixtures of spherical gaussians moment methods and spectral decompositions"
    - "compressed sensing off the grid"

Connected through hyperedge HE-8 (induced by source paper "how to refute a random csp"):
  with members:
    - "complexity theoretic limitations on learning halfspaces"
    - "sum of squares lower bounds from pairwise independence"
    - "tensor prediction rademacher complexity and random 3 xor"
    - "analysis of boolean functions"
    - "subsampled power iteration a unified algorithm for block models and planted csp s"
    - "sum of squares proofs and the quest toward optimal algorithms"
    - "from average case complexity to improper learning complexity"
    - "on the complexity of random satisfiability problems with planted solutions"

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)