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 "attention based deep distance metric learning for aspect phrase grouping"):
  Abstract: Aspect-phrase grouping is an important task for aspect finding in aspect-level sentiment analysis and it is a challenging problem due to polysemy and its context dependency. In this paper we propose an Attention-based Deep Distance Metric Learning (ADDML)method, which is more beneficial for clustering by considering aspect-phrase representation as well as context representation and their combination. Firstly, we feed word embeddings of aspect-phrases and its contexts into an attentionbased network to learn feature representation of contexts. Then, both of aspect-phrase embedding and context embedding as the input of a multilayer perceptron which is used to learn deep feature subspace, under which the distance of each intra-grouped pair is smaller and that of each intergrouped pair is bigger, respectively. After obtaining the learned representations, we use K-means to cluster them. Our extensive experimental study on four domain review datasets shows that the proposed method outperforms baseline methods.

Includes members:
  Member "a neural attention model for abstractive sentence summarization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a 3 accelerating attention mechanisms in neural networks with approximation")
  Member "effective approaches to attention based neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "attention over parameters for dialogue systems")
  Member "deep feature learning with relative distance comparison for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "magnifiernet towards semantic regularization and fusion for person re identification")
  Member "constrained deep metric learning for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a quadruplet loss for enforcing semantically coherent embeddings in multi output classification problems")
  Member "deep metric learning for practical person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "person recognition in personal photo collections")
  Member "convolutional neural networks for sentence classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "a convolutional neural network for modelling sentences":
    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 "distributed representations of words and phrases and their compositionality":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")

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)