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: "evolving culture vs local minima"
  Abstract: We propose a theory that relates difficulty of learning in deep architectures to culture and language. It is articulated around the following hypotheses: (1) learning in an individual human brain is hampered by the presence of effective local minima; (2) this optimization difficulty is particularly important when it comes to learning higher-level abstractions, i.e., concepts that cover a vast and highly-nonlinear span of sensory configurations; (3) such high-level abstractions are best represented in brains by the composition of many levels of representation, i.e., by deep architectures; (4) a human brain can learn such high-level abstractions if guided by the signals produced by other humans, which act as hints or indirect supervision for these high-level abstractions; and (5), language and the recombination and optimization of mental concepts provide an efficient evolutionary recombination operator, and this gives rise to rapid search in the space of communicable ideas that help humans build up better high-level internal representations of their world. These hypotheses put together imply that human culture and the evolution of ideas have been crucial to counter an optimization difficulty: this optimization difficulty would otherwise make it very difficult for human brains to capture high-level knowledge of the world. The theory is grounded in experimental observations of the difficulties of training deep artificial neural networks. Plausible consequences of this theory for the efficiency of cultural evolutions are sketched.

Connected through hyperedge HE-1 (induced by source paper "incentivizing the emergence of grounded discrete communication between general agents"):
  with members:
    - "on the pitfalls of measuring emergent communication"
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "compositional obverter communication learning from raw visual input"
    - "scalable agent alignment via reward modeling a research direction"
    - "emergence of linguistic communication from referential games with symbolic and pixel input"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"
    - "natural language does not emerge naturally in multi agent dialog"
    - "emergence of grounded compositional language in multi agent populations"

Connected through hyperedge HE-2 (induced by source paper "emergence of compositional language with deep generational transmission"):
  with members:
    - "compositional obverter communication learning from raw visual input"
    - "translating neuralese"
    - "emergence of linguistic communication from referential games with symbolic and pixel input"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"
    - "large scale evolution of image classifiers"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "natural language does not emerge naturally in multi agent dialog"
    - "learning cooperative visual dialog agents with deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "dragon a computation graph virtual machine based deep learning framework"):
  with members:
    - "fast r cnn"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "adam a method for stochastic optimization"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-4 (induced by source paper "deep learning of representations looking forward"):
  with members:
    - "a semantic matching energy function for learning with multi relational data"
    - "knowledge matters importance of prior information for optimization"
    - "big neural networks waste capacity"
    - "deep generative stochastic networks trainable by backprop"
    - "generalized denoising auto encoders as generative models"
    - "estimating or propagating gradients through stochastic neurons"
    - "stochastic pooling for regularization of deep convolutional neural networks"
    - "revisiting natural gradient for deep networks"

Connected through hyperedge HE-5 (induced by source paper "knowledge matters importance of prior information for optimization"):
  with members:
    - "the loss surfaces of multilayer networks"
    - "identifying and attacking the saddle point problem in high dimensional non convex optimization"
    - "big neural networks waste capacity"
    - "pushing stochastic gradient towards second order methods backpropagation learning with transformations in nonlinearities"
    - "a generative process for sampling contractive auto encoders"
    - "practical recommendations for gradient based training of deep architectures"
    - "representation learning a review and new perspectives"
    - "adadelta an adaptive learning rate method"

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)