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: "replication issues in syntax based aspect extraction for opinion mining"
  Abstract: Reproducing experiments is an important instrument to validate previous work and build upon existing approaches. It has been tackled numerous times in different areas of science. In this paper, we introduce an empirical replicability study of three well-known algorithms for syntactic centric aspect-based opinion mining. We show that reproducing results continues to be a difficult endeavor, mainly due to the lack of details regarding preprocessing and parameter setting, as well as due to the absence of available implementations that clarify these details. We consider these are important threats to validity of the research on the field, specifically when compared to other problems in NLP where public datasets and code availability are critical validity components. We conclude by encouraging code-based research, which we think has a key role in helping researchers to understand the meaning of the state-of-the-art better and to generate continuous advances.

Connected through hyperedge HE-1 (induced by source paper "revisiting the hierarchical multiscale lstm"):
  with members:
    - "do latent tree learning models identify meaningful structure in sentences"
    - "encoding of phonology in a recurrent neural model of grounded speech"
    - "representations of language in a model of visually grounded speech signal"
    - "recurrent neural network grammars"
    - "noisy activation functions"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "a fast unified model for parsing and sentence understanding"
    - "character level question answering with attention"

Connected through hyperedge HE-2 (induced by source paper "bringing replication and reproduction together with generalisability in nlp three reproduction studies for target dependent sentiment analysis"):
  with members:
    - "multi task learning of pairwise sequence classification tasks over disparate label spaces"
    - "assessing state of the art sentiment models on state of the art sentiment datasets"
    - "mining fine grained opinions on closed captions of youtube videos with an attention rnn"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "learning to attend via word aspect associative fusion for aspect based sentiment analysis"
    - "thumbs up or thumbs down semantic orientation applied to unsupervised classification of reviews"
    - "thumbs up sentiment classification using machine learning techniques"

Connected through hyperedge HE-3 (induced by source paper "replicability analysis for natural language processing testing significance with multiple datasets"):
  with members:
    - "mimicking word embeddings using subword rnns"
    - "neural structural correspondence learning for domain adaptation"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "polyglot distributed word representations for multilingual nlp"
    - "distributed representations of words and phrases and their compositionality"

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)