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: "complexity theoretic limitations on learning dnf s"
  Abstract: Using the recently developed framework of [Daniely et al, 2014], we show that under a natural assumption on the complexity of refuting random K-SAT formulas, learning DNF formulas is hard. Furthermore, the same assumption implies the hardness of learning intersections of $\omega(\log(n))$ halfspaces, agnostically learning conjunctions, as well as virtually all (distribution free) learning problems that were previously shown hard (under complexity assumptions).

Connected through hyperedge HE-1 (induced by source paper "toward deeper understanding of neural networks the power of initialization and a dual view on expressivity"):
  with members:
    - "deep neural networks with random gaussian weights a universal classification strategy"
    - "deep convolutional networks are hierarchical kernel machines"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "on the equivalence between kernel quadrature rules and random feature expansions"
    - "complexity theoretic limitations on learning halfspaces"
    - "norm based capacity control in neural networks"
    - "train faster generalize better stability of stochastic gradient descent"
    - "path sgd path normalized optimization in deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "distribution free learning with local queries"):
  with members:
    - "learning using 1 local membership queries"
    - "learning using local membership queries"

Connected through hyperedge HE-3 (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"
    - "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"
    - "the complexity of learning halfspaces using generalized linear methods"

Connected through hyperedge HE-4 (induced by source paper "learning using 1 local membership queries"):
  with members:
    - "from average case complexity to improper learning complexity"
    - "more data speeds up training time in learning halfspaces over sparse vectors"
    - "learning using local membership queries"
    - "a sentimental education sentiment analysis using subjectivity summarization based on minimum cuts"

Connected through hyperedge HE-5 (induced by source paper "a ptas for agnostically learning halfspaces"):
  with members:
    - "complexity theoretic limitations on learning halfspaces"
    - "the power of localization for efficiently learning linear separators with noise"
    - "from average case complexity to improper learning complexity"
    - "the complexity of learning halfspaces using generalized linear methods"
    - "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"

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)