human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "summarizing opinions aspect extraction meets sentiment prediction and they are both weakly supervised"):
  Abstract: We present a neural framework for opinion summarization from online product reviews which is knowledge-lean and only requires light supervision (e.g., in the form of product domain labels and user-provided ratings). Our method combines two weakly supervised components to identify salient opinions and form extractive summaries from multiple reviews: an aspect extractor trained under a multi-task objective, and a sentiment predictor based on multiple instance learning. We introduce an opinion summarization dataset that includes a training set of product reviews from six diverse domains and human-annotated development and test sets with gold standard aspect annotations, salience labels, and opinion summaries. Automatic evaluation shows significant improvements over baselines, and a large-scale study indicates that our opinion summaries are preferred by human judges according to multiple criteria.

Includes members:
  Member "multiple instance learning networks for fine grained sentiment analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "weakly supervised attention networks for fine grained opinion mining and public health")
  Member "best worst scaling more reliable than rating scales a case study on sentiment intensity annotation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "studying the effects of cognitive biases in evaluation of conversational agents")
  Member "graph based neural multi document summarization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "improving the similarity measure of determinantal point processes for extractive multi document summarization")
  Member "neural network based abstract generation for opinions and arguments":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "what is this article about extreme summarization with topic aware convolutional neural networks")
  Member "unsupervised word and dependency path embeddings for aspect term extraction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "comprehensive analysis of aspect term extraction methods using various text embeddings")
  Member "better document level sentiment analysis from rst discourse parsing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "structsum incorporating latent and explicit sentence dependencies for single document summarization")
  Member "image based recommendations on styles and substitutes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

Question: Please tell me which class the source paper 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)