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: "predicting the effectiveness of self training application to sentiment classification"
  Abstract: The goal of this paper is to investigate the connection between the performance gain that can be obtained by selftraining and the similarity between the corpora used in this approach. Self-training is a semi-supervised technique designed to increase the performance of machine learning algorithms by automatically classifying instances of a task and adding these as additional training material to the same classifier. In the context of language processing tasks, this training material is mostly an (annotated) corpus. Unfortunately self-training does not always lead to a performance increase and whether it will is largely unpredictable. We show that the similarity between corpora can be used to identify those setups for which self-training can be beneficial. We consider this research as a step in the process of developing a classifier that is able to adapt itself to each new test corpus that it is presented with.

Connected through hyperedge HE-1 (induced by source paper "dynamic self training framework for graph convolutional networks"):
  with members:
    - "multi stage self supervised learning for graph convolutional networks"
    - "pitfalls of graph neural network evaluation"
    - "representation learning on graphs with jumping knowledge networks"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "bootstrapped graph diffusions exposing the power of nonlinearity"
    - "node2vec scalable feature learning for networks"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"

Connected through hyperedge HE-2 (induced by source paper "learning how to self learn enhancing self training using neural reinforcement learning"):
  with members:
    - "split and rephrase"
    - "learning how to active learn a deep reinforcement learning approach"
    - "sentence simplification with deep reinforcement learning"
    - "deep reinforcement learning for dialogue generation"
    - "learning to translate in real time with neural machine translation"
    - "enriching word vectors with subword information"
    - "an actor critic algorithm for sequence prediction"
    - "rationalizing neural predictions"

Connected through hyperedge HE-3 (induced by source paper "strong baselines for neural semi supervised learning under domain shift"):
  with members:
    - "on the state of the art of evaluation in neural language models"
    - "stronger baselines for trustable results in neural machine translation"
    - "model transfer for tagging low resource languages using a bilingual dictionary"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "to normalize or not to normalize the impact of normalization on part of speech tagging"
    - "online updating of word representations for part of speech tagging"
    - "temporal ensembling for semi supervised learning"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"

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.CL(Computation and Language)