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 "location relative attention mechanisms for robust long form speech synthesis"):
  Abstract: Despite the ability to produce human-level speech for in-domain text, attention-based end-to-end text-to-speech (TTS) systems suffer from text alignment failures that increase in frequency for out-of-domain text. We show that these failures can be addressed using simple location-relative attention mechanisms that do away with content-based query/key comparisons. We compare two families of attention mechanisms: location-relative GMM-based mechanisms and additive energy-based mechanisms. We suggest simple modifications to GMM-based attention that allow it to align quickly and consistently during training, and introduce a new location-relative attention mechanism to the additive energy-based family, called Dynamic Convolution Attention (DCA). We compare the various mechanisms in terms of alignment speed and consistency during training, naturalness, and ability to generalize to long utterances, and conclude that GMM attention and DCA can generalize to very long utterances, while preserving naturalness for shorter, in-domain utterances.

Includes members:
  Member "robust sequence to sequence acoustic modeling with stepwise monotonic attention for neural tts":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "teacher student training for robust tacotron based tts")
  Member "effective use of variational embedding capacity in expressive end to end speech synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "semi supervised generative modeling for controllable speech synthesis")
  Member "efficient neural audio synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "what is the state of neural network pruning")
  Member "representation mixing for tts synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "libritts a corpus derived from librispeech for text to speech")
  Member "robust and fine grained prosody control of end to end speech synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "direct speech to speech translation with a sequence to sequence model")
  Member "towards end to end prosody transfer for expressive speech synthesis with tacotron":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "et gan cross language emotion transfer based on cycle consistent generative adversarial networks")
  Member "online and linear time attention by enforcing monotonic alignments":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "evaluating sequence to sequence models for handwritten text recognition")
  Member "tacotron towards end to end speech synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "semi supervised neural architecture search")

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)