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: "a statistical parsing framework for sentiment classification"
  Abstract: We present a statistical parsing framework for sentence-level sentiment classification in this article. Unlike previous works that employ syntactic parsing results for sentiment analysis, we develop a statistical parser to directly analyze the sentiment structure of a sentence. We show that complicated phenomena in sentiment analysis (e.g., negation, intensification, and contrast) can be handled the same as simple and straightforward sentiment expressions in a unified and probabilistic way. We formulate the sentiment grammar upon Context-Free Grammars (CFGs), and provide a formal description of the sentiment parsing framework. We develop the parsing model to obtain possible sentiment parse trees for a sentence, from which the polarity model is proposed to derive the sentiment strength and polarity, and the ranking model is dedicated to selecting the best sentiment tree. We train the parser directly from examples of sentences annotated only with sentiment polarity labels but without any syntactic annotations or polarity annotations of constituents within sentences. Therefore we can obtain training data easily. In particular, we train a sentiment parser, s.parser, from a large amount of review sentences with users' ratings as rough sentiment polarity labels. Extensive experiments on existing benchmark datasets show significant improvements over baseline sentiment classification approaches.

Connected through hyperedge HE-1 (induced by source paper "latent variable sentiment grammar"):
  with members:
    - "deep contextualized word representations"
    - "gaussian mixture latent vector grammars"
    - "learned in translation contextualized word vectors"
    - "compositional distributional semantics with long short term memory"
    - "improved semantic representations from tree structured long short term memory networks"
    - "when are tree structures necessary for deep learning of representations"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "sentiment composition of words with opposing polarities"):
  with members:
    - "capturing reliable fine grained sentiment associations by crowdsourcing and best worst scaling"
    - "the effect of negators modals and degree adverbs on sentiment composition"
    - "improved semantic representations from tree structured long short term memory networks"
    - "nrc canada building the state of the art in sentiment analysis of tweets"
    - "efficient estimation of word representations in vector space"
    - "crowdsourcing a word emotion association lexicon"

Connected through hyperedge HE-3 (induced by source paper "convolutional neural networks for sentence classification"):
  with members:
    - "cnn features off the shelf an astounding baseline for recognition"
    - "a convolutional neural network for modelling sentences"
    - "speech recognition with deep recurrent neural networks"
    - "distributed representations of words and phrases and their compositionality"
    - "adadelta an adaptive learning rate method"
    - "improving neural networks by preventing co adaptation of feature detectors"
    - "natural language processing almost from scratch"
    - "seeing stars exploiting class relationships for sentiment categorization with respect to rating scales"

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)