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 "dynamic prosody generation for speech synthesis using linguistics driven acoustic embedding selection"):
  Abstract: Recent advances in Text-to-Speech (TTS) have improved quality and naturalness to near-human capabilities when considering isolated sentences. But something which is still lacking in order to achieve human-like communication is the dynamic variations and adaptability of human speech. This work attempts to solve the problem of achieving a more dynamic and natural intonation in TTS systems, particularly for stylistic speech such as the newscaster speaking style. We propose a novel embedding selection approach which exploits linguistic information, leveraging the speech variability present in the training dataset. We analyze the contribution of both semantic and syntactic features. Our results show that the approach improves the prosody and naturalness for complex utterances as well as in Long Form Reading (LFR).

Includes members:
  Member "what do you learn from context probing for sentence structure in contextualized word representations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on contextual embeddings")
  Member "exploiting syntactic features in a parsed tree to improve end to end tts":
    also connected through other hyperedges:
      (none)
  Member "predicting expressive speaking style from text in end to end speech synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "in other news a bi style text to speech model for synthesizing newscaster voice with limited data")
  Member "an empirical analysis of the correlation of syntax and prosody":
    also connected through other hyperedges:
      (none)
  Member "effects of word embeddings on neural network based pitch accent detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "predicting prosodic prominence from text with pre trained contextualized word representations")
  Member "straight to the tree constituency parsing with neural syntactic distance":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "are pre trained language models aware of phrases simple but strong baselines for grammar induction")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "expressive speech synthesis via modeling expressions with variational autoencoder":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "semi supervised generative modeling for controllable speech synthesis")

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)