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 "parameter free sentence embedding via orthogonal basis"):
  Abstract: We propose a simple and robust non-parameterized approach for building sentence representations. Inspired by the Gram-Schmidt Process in geometric theory, we build an orthogonal basis of the subspace spanned by a word and its surrounding context in a sentence. We model the semantic meaning of a word in a sentence based on two aspects. One is its relatedness to the word vector subspace already spanned by its contextual words. The other is the word's novel semantic meaning which shall be introduced as a new basis vector perpendicular to this existing subspace. Following this motivation, we develop an innovative method based on orthogonal basis to combine pre-trained word embeddings into sentence representations. This approach requires zero parameters, along with efficient inference performance. We evaluate our approach on 11 downstream NLP tasks. Our model shows superior performance compared with non-parameterized alternatives and it is competitive to other approaches relying on either large amounts of labelled data or prolonged training time.

Includes members:
  Member "learning semantic textual similarity from conversations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "toward interpretability of dual encoder models for dialogue response suggestions")
  Member "a la carte embedding cheap but effective induction of semantic feature vectors":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "improving neural language modeling via adversarial training")
  Member "universal sentence encoder":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "contextual lensing of universal sentence representations")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "unsupervised learning of sentence embeddings using compositional n gram features":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sentence analogies exploring linguistic relationships and regularities in sentence embeddings")
  Member "an efficient framework for learning sentence representations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "contextual lensing of universal sentence representations")
  Member "concatenated p mean word embeddings as universal cross lingual sentence representations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sbert wk a sentence embedding method by dissecting bert based word models")
  Member "learning general purpose distributed sentence representations via large scale multi task learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a survey on contextual embeddings")

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)