human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "multi task recurrent model for speech and speaker recognition"
  Abstract: Although highly correlated, speech and speaker recognition have been regarded as two independent tasks and studied by two communities. This is certainly not the way that people behave: we decipher both speech content and speaker traits at the same time. This paper presents a unified model to perform speech and speaker recognition simultaneously and altogether. The model is based on a unified neural network where the output of one task is fed to the input of the other, leading to a multi-task recurrent network. Experiments show that the joint model outperforms the task-specific models on both the two tasks.

Connected through hyperedge HE-1 (induced by source paper "to reverse the gradient or not an empirical comparison of adversarial and multi task learning in speech recognition"):
  with members:
    - "cross domain regularization for neural ranking models using adversarial learning"
    - "domain adversarial training for accented speech recognition"
    - "english conversational telephone speech recognition by humans and machines"
    - "letter based speech recognition with gated convnets"
    - "an adversarial regularisation for semi supervised training of structured output neural networks"
    - "wav2letter an end to end convnet based speech recognition system"
    - "an overview of multi task learning in deep neural networks"
    - "fader networks manipulating images by sliding attributes"

Connected through hyperedge HE-2 (induced by source paper "text independent speaker verification based on deep neural networks and segmental dynamic time warping"):
  with members:
    - "deep speaker an end to end neural speaker embedding system"
    - "improved deep speaker feature learning for text dependent speaker recognition"

Connected through hyperedge HE-3 (induced by source paper "collaborative learning for language and speaker recognition"):
  with members:
    - "end to end attention based text dependent speaker verification"
    - "transfer learning for speech and language processing"
    - "improved deep speaker feature learning for text dependent speaker recognition"
    - "parallel training of dnns with natural gradient and parameter averaging"

Connected through hyperedge HE-4 (induced by source paper "multi task recurrent model for true multilingual speech recognition"):
  with members:
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "long short term memory based recurrent neural network architectures for large vocabulary speech recognition"
    - "parallel training of dnns with natural gradient and parameter averaging"

Question: Please tell me which class the center node 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)