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: "deep learning and hierarchal generative models"
  Abstract: It is argued that deep learning is efficient for data that is generated from hierarchal generative models. Examples of such generative models include wavelet scattering networks, functions of compositional structure, and deep rendering models. Unfortunately so far, for all such models, it is either not rigorously known that they can be learned efficiently, or it is not known that "deep algorithms" are required in order to learn them. #R##N#We propose a simple family of "generative hierarchal models" which can be efficiently learned and where "deep" algorithm are necessary for learning. Our definition of "deep" algorithms is based on the empirical observation that deep nets necessarily use correlations between features. More formally, we show that in a semi-supervised setting, given access to low-order moments of the labeled data and all of the unlabeled data, it is information theoretically impossible to perform classification while at the same time there is an efficient algorithm, that given all labelled and unlabeled data, perfectly labels all unlabelled data with high probability. #R##N#For the proof, we use and strengthen the fact that Belief Propagation does not admit a good approximation in terms of linear functions.

Connected through hyperedge HE-1 (induced by source paper "parallels between phase transitions and circuit complexity"):
  with members:
    - "accuracy memory tradeoffs and phase transitions in belief propagation"
    - "reducibility and computational lower bounds for problems with planted sparse structure"
    - "benefits of depth in neural networks"
    - "community detection thresholds and the weak ramanujan property"

Connected through hyperedge HE-2 (induced by source paper "the circuit complexity of inference"):
  with members:
    - "benefits of depth in neural networks"
    - "learning functions when is deep better than shallow"

Connected through hyperedge HE-3 (induced by source paper "a provably correct algorithm for deep learning that actually works"):
  with members:
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "recovery guarantees for one hidden layer neural networks"
    - "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"
    - "sgd learns over parameterized networks that provably generalize on linearly separable data"
    - "when is a convolutional filter easy to learn"
    - "convexified convolutional neural networks"
    - "learning halfspaces and neural networks with random initialization"

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)