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 "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"):
  Abstract: Semantic Textual Similarity (STS) measures the meaning similarity of sentences. Applications include machine translation (MT), summarization, generation, question answering (QA), short answer grading, semantic search, dialog and conversational systems. The STS shared task is a venue for assessing the current state-of-the-art. The 2017 task focuses on multilingual and cross-lingual pairs with one sub-track exploring MT quality estimation (MTQE) data. The task obtained strong participation from 31 teams, with 17 participating in all language tracks. We summarize performance and review a selection of well performing methods. Analysis highlights common errors, providing insight into the limitations of existing models. To support ongoing work on semantic representations, the STS Benchmark is introduced as a new shared training and evaluation set carefully selected from the corpus of English STS shared task data (2012-2017).

Includes members:
  Member "unsupervised learning of sentence embeddings using compositional n gram features":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sentence analogies exploring linguistic relationships and regularities in sentence embeddings")
  Member "neobility at semeval 2017 task 1 an attention based sentence similarity model":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unsupervised pidgin text generation by pivoting english data and self training")
  Member "revisiting recurrent networks for paraphrastic sentence embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "structural aware sentence similarity with recursive optimal transport")
  Member "matrix factorization using window sampling and negative sampling for improved word representations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a common semantic space for monolingual and cross lingual meta embeddings")
  Member "enhancing and combining sequential and tree lstm for natural language inference":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "zero shot cross lingual transfer with meta learning")
  Member "an empirical evaluation of doc2vec with practical insights into document embedding generation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "can x2vec save lives integrating graph and language embeddings for automatic mental health classification")
  Member "learning distributed representations of sentences from unlabelled data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "contextual lensing of universal sentence representations")
  Member "charagram embedding words and sentences via character n grams":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "improving candidate generation for low resource cross lingual entity linking")

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)