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: "improved deep speaker feature learning for text dependent speaker recognition"
  Abstract: A deep learning approach has been proposed recently to derive speaker identifies (d-vector) by a deep neural network (DNN). This approach has been applied to text-dependent speaker recognition tasks and shows reasonable performance gains when combined with the conventional i-vector approach. Although promising, the existing d-vector implementation still can not compete with the i-vector baseline. This paper presents two improvements for the deep learning approach: a phonedependent DNN structure to normalize phone variation, and a new scoring approach based on dynamic time warping (DTW). Experiments on a text-dependent speaker recognition task demonstrated that the proposed methods can provide considerable performance improvement over the existing d-vector implementation.

Connected through hyperedge HE-1 (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"
    - "multi task recurrent model for speech and speaker recognition"

Connected through hyperedge HE-2 (induced by source paper "deep speaker feature learning for text independent speaker verification"):
  with members:
    - "end to end attention based text dependent speaker verification"
    - "parallel training of dnns with natural gradient and parameter averaging"

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"
    - "multi task recurrent model for speech and speaker recognition"
    - "transfer learning for speech and language processing"
    - "parallel training of dnns with natural gradient and parameter averaging"

Connected through hyperedge HE-4 (induced by source paper "multi task recurrent model for speech and speaker recognition"):
  with members:
    - "transfer learning for speech and language processing"
    - "end to end text dependent speaker verification"
    - "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)