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 "searching for legal clauses by analogy few shot semantic retrieval shared task"):
  Abstract: We introduce a novel shared task for semantic retrieval from legal texts, where one is expected to perform a so-called contract discovery -- extract specified legal clauses from documents given a few examples of similar clauses from other legal acts. The task differs substantially from conventional NLI and legal information extraction shared tasks. Its specification is followed with evaluation of multiple k-NN based solutions within the unified framework proposed for this branch of methods. It is shown that state-of-the-art pre-trained encoders fail to provide satisfactory results on the task proposed, whereas Language Model based solutions perform well, especially when unsupervised fine-tuning is applied. In addition to the ablation studies, the questions regarding relevant text fragments detection accuracy depending on number of examples available were addressed. In addition to dataset and reference results, legal-specialized LMs were made publicly available.

Includes members:
  Member "generalizing from a few examples a survey on few shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a two stage approach to few shot learning for image recognition")
  Member "efficient sentence embedding using discrete cosine transform":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sentence analogies exploring linguistic relationships and regularities in sentence embeddings")
  Member "sentence bert sentence embeddings using siamese bert networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "contextual lensing of universal sentence representations")
  Member "no training required exploring random encoders for sentence classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "contextual lensing of universal sentence representations")
  Member "vector of locally aggregated word embeddings vlawe a novel document level representation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "efficient sentence embedding via semantic subspace analysis")
  Member "moverscore text generation evaluating with contextualized embeddings and earth mover distance":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bert as a teacher contextual embeddings for sequence level reward")
  Member "cbow is not all you need combining cbow with the compositional matrix space model":
    also connected through other hyperedges:
      (none)
  Member "roberta a robustly optimized bert pretraining approach":
    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")

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)