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 "embedding text in hyperbolic spaces"):
  Abstract: Natural language text exhibits hierarchical structure in a variety of respects. Ideally, we could incorporate our prior knowledge of this hierarchical structure into unsupervised learning algorithms that work on text data. Recent work by Nickel & Kiela (2017) proposed using hyperbolic instead of Euclidean embedding spaces to represent hierarchical data and demonstrated encouraging results when embedding graphs. In this work, we extend their method with a re-parameterization technique that allows us to learn hyperbolic embeddings of arbitrarily parameterized objects. We apply this framework to learn word and sentence embeddings in hyperbolic space in an unsupervised manner from text corpora. The resulting embeddings seem to encode certain intuitive notions of hierarchy, such as word-context frequency and phrase constituency. However, the implicit continuous hierarchy in the learned hyperbolic space makes interrogating the model's learned hierarchies more difficult than for models that learn explicit edges between items. The learned hyperbolic embeddings show improvements over Euclidean embeddings in some -- but not all -- downstream tasks, suggesting that hierarchical organization is more useful for some tasks than others.

Includes members:
  Member "trimming and improving skip thought vectors":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an end to end neural network framework for text clustering")
  Member "hierarchical embeddings for hypernymy detection and directionality":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discriminative topic mining via category name guided text embedding")
  Member "discourse based objectives for fast unsupervised sentence representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pre training tasks for embedding based large scale retrieval")
  Member "dissent sentence representation learning from explicit discourse relations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "probing what different nlp tasks teach machines about function word comprehension")
  Member "unsupervised hypernym detection by distributional inclusion vector embedding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "composition of sentence embeddings lessons from statistical relational learning")
  Member "specialising word vectors for lexical entailment":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "inducing relational knowledge from bert")
  Member "a broad coverage challenge corpus for sentence understanding through inference":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "speeding up context based sentence representation learning with non autoregressive convolutional decoding":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "exploiting invertible decoders for unsupervised sentence representation learning")

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)