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: "coherent to diffuse power ratio estimation for dereverberation"
  Abstract: The estimation of the time- and frequency-dependent coherent-to-diffuse power ratio (CDR) from the measured spatial coherence between two omnidirectional microphones is investigated. Known CDR estimators are formulated in a common framework, illustrated using a geometric interpretation in the complex plane, and investigated with respect to bias and robustness towards model errors. Several novel unbiased CDR estimators are proposed, and it is shown that knowledge of either the direction of arrival (DOA) of the target source or the coherence of the noise field is sufficient for unbiased CDR estimation. The validity of the model for the application of CDR estimates to dereverberation is investigated using measured and simulated impulse responses. A CDR-based dereverberation system is presented and evaluated using signal-based quality measures as well as automatic speech recognition accuracy. The results show that the proposed unbiased estimators have a practical advantage over existing estimators, and that the proposed DOA-independent estimator can be used for effective blind dereverberation.

Connected through hyperedge HE-1 (induced by source paper "narrow band deep filtering for multichannel speech enhancement"):
  with members:
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "supervised speech separation based on deep learning an overview"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "joint alignment of multiple point sets with batch and incremental expectation maximization"):
  with members:
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "automatic detection of calibration grids in time of flight images"
    - "point set registration coherent point drift"

Connected through hyperedge HE-3 (induced by source paper "multichannel online dereverberation based on spectral magnitude inverse filtering"):
  with members:
    - "multichannel speech separation and enhancement using the convolutive transfer function"
    - "speech dereverberation using non negative convolutive transfer function and spectro temporal modeling"

Connected through hyperedge HE-4 (induced by source paper "robust coherence based spectral enhancement for speech recognition in adverse real world environments"):
  with members:
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "spatial diffuseness features for dnn based speech recognition in noisy and reverberant environments"

Connected through hyperedge HE-5 (induced by source paper "estimation of the direct path relative transfer function for supervised sound source localization"):
  with members:
    - "binaural coherent to diffuse ratio estimation for dereverberation using an itd model"
    - "co localization of audio sources in images using binaural features and locally linear regression"
    - "acoustic space learning for sound source separation and localization on binaural manifolds"

Connected through hyperedge HE-6 (induced by source paper "speech dereverberation using non negative convolutive transfer function and spectro temporal modeling"):
  with members:
    - "a state space approach to dynamic nonnegative matrix factorization"
    - "supervised and unsupervised speech enhancement using nonnegative matrix factorization"

Connected through hyperedge HE-7 (induced by source paper "an improved uncertainty decoding scheme with weighted samples for dnn hmm hybrid systems"):
  with members:
    - "spatial diffuseness features for dnn based speech recognition in noisy and reverberant environments"

Connected through hyperedge HE-8 (induced by source paper "robust coherence based spectral enhancement for distant speech recognition"):
  with members:
    - "spatial diffuseness features for dnn based speech recognition in noisy and reverberant environments"

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.SD(Sound)