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: "multiple instance learning networks for fine grained sentiment analysis"
  Abstract: We consider the task of fine-grained sentiment analysis from the perspective of multiple instance learning (MIL). Our neural model is trained on document sentiment labels, and learns to predict the sentiment of text segments, i.e. sentences or elementary discourse units (EDUs), without segment-level supervision. We introduce an attention-based polarity scoring method for identifying positive and negative text snippets and a new dataset which we call SPOT (as shorthand for Segment-level POlariTy annotations) for evaluating MIL-style sentiment models like ours. Experimental results demonstrate superior performance against multiple baselines, whereas a judgement elicitation study shows that EDU-level opinion extraction produces more informative summaries than sentence-based alternatives.

Connected through hyperedge HE-1 (induced by source paper "weakly supervised attention networks for fine grained opinion mining and public health"):
  with members:
    - "attention based deep multiple instance learning"
    - "zero shot sequence labeling transferring knowledge from sentences to tokens"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deriving machine attention from human rationales"
    - "structured attention networks"
    - "multiple instance learning a survey of problem characteristics and applications"
    - "revisiting recurrent networks for paraphrastic sentence embeddings"
    - "neural attention models for sequence classification analysis and application to key term extraction and dialogue act detection"

Connected through hyperedge HE-2 (induced by source paper "text length adaptation in sentiment classification"):
  with members:
    - "strong baselines for neural semi supervised learning under domain shift"
    - "a survey on deep transfer learning"
    - "aspect sentiment model for micro reviews"
    - "learning word vectors for 157 languages"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "adversarial deep averaging networks for cross lingual sentiment classification"
    - "neural structural correspondence learning for domain adaptation"

Connected through hyperedge HE-3 (induced by source paper "weakly supervised domain detection"):
  with members:
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "attention is all you need"
    - "variations of the similarity function of textrank for automated summarization"
    - "very deep convolutional networks for text classification"
    - "rationale augmented convolutional neural networks for text classification"
    - "domain separation networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "predicting discourse structure using distant supervision from sentiment"):
  with members:
    - "evaluating discourse in structured text representations"
    - "learning structured text representations"
    - "neural discourse structure for text categorization"
    - "cross lingual rst discourse parsing"
    - "tweetime a minimally supervised method for recognizing and normalizing time expressions in twitter"
    - "character level convolutional networks for text classification"
    - "better document level sentiment analysis from rst discourse parsing"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-5 (induced by source paper "cawa an attention network for credit attribution"):
  with members:
    - "text segmentation on multilabel documents a distant supervised approach"
    - "attention based neural text segmentation"
    - "text segmentation as a supervised learning task"
    - "rationalizing neural predictions"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "advances in domain independent linear text segmentation"

Connected through hyperedge HE-6 (induced by source paper "learning maximally predictive prototypes in multiple instance learning"):
  with members:
    - "c mil continuation multiple instance learning for weakly supervised object detection"
    - "attention based deep multiple instance learning"
    - "pcl proposal cluster learning for weakly supervised object detection"
    - "adaptive pooling operators for weakly labeled sound event detection"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "preprocessing methods and pipelines of data mining an overview"):
  with members:
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "summarizing opinions aspect extraction meets sentiment prediction and they are both weakly supervised"):
  with members:
    - "best worst scaling more reliable than rating scales a case study on sentiment intensity annotation"
    - "graph based neural multi document summarization"
    - "neural network based abstract generation for opinions and arguments"
    - "unsupervised word and dependency path embeddings for aspect term extraction"
    - "better document level sentiment analysis from rst discourse parsing"
    - "image based recommendations on styles and substitutes"
    - "adam a method for stochastic optimization"
    - "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)