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 for multi label classification"
  Abstract: In multi-label classification, the main focus has been to develop ways of learning the underlying dependencies between labels, and to take advantage of this at classification time. Developing better feature-space representations has been predominantly employed to reduce complexity, e.g., by eliminating non-helpful feature attributes from the input space prior to (or during) training. This is an important task, since many multi-label methods typically create many different copies or views of the same input data as they transform it, and considerable memory can be saved by taking advantage of redundancy. In this paper, we show that a proper development of the feature space can make labels less interdependent and easier to model and predict at inference time. For this task we use a deep learning approach with restricted Boltzmann machines. We present a deep network that, in an empirical evaluation, outperforms a number of competitive methods from the literature

Connected through hyperedge HE-1 (induced by source paper "cascademl an automatic neural network architecture evolution and training algorithm for multi label classification"):
  with members:
    - "automated multi label classification based on ml plan"
    - "multi label learning based deep transfer neural network for facial attribute classification"
    - "hcp a flexible cnn framework for multi label image classification"

Connected through hyperedge HE-2 (induced by source paper "multi label classification search space in the meka software"):
  with members:
    - "efficient monte carlo methods for multi dimensional learning with classifier chains"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "estimating continuous distributions in bayesian classifiers"
    - "auto weka combined selection and hyperparameter optimization of classification algorithms"
    - "locally weighted naive bayes"

Connected through hyperedge HE-3 (induced by source paper "tips guidelines and tools for managing multi label datasets the mldr datasets r package and the cometa data repository"):
  with members:
    - "a practical tutorial on autoencoders for nonlinear feature fusion taxonomy models software and guidelines"
    - "dealing with difficult minority labels in imbalanced mutilabel data sets"
    - "remedial hwr tackling multilabel imbalance through label decoupling and data resampling hybridization"
    - "hcp a flexible cnn framework for multi label image classification"

Connected through hyperedge HE-4 (induced by source paper "joint binary neural network for multi label learning with applications to emotion classification"):
  with members:
    - "deep neural networks for czech multi label document classification"
    - "neural machine translation by jointly learning to align and translate"
    - "distributed representations of words and phrases and their compositionality"
    - "large scale multi label text classification revisiting neural networks"

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)