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: "spatial diffuseness features for dnn based speech recognition in noisy and reverberant environments"
  Abstract: We propose a spatial diffuseness feature for deep neural network (DNN)-based automatic speech recognition to improve recognition accuracy in reverberant and noisy environments. The feature is computed in real-time from multiple microphone signals without requiring knowledge or estimation of the direction of arrival, and represents the relative amount of diffuse noise in each time and frequency bin. It is shown that using the diffuseness feature as an additional input to a DNN-based acoustic model leads to a reduced word error rate for the REVERB challenge corpus, both compared to logmelspec features extracted from noisy signals, and features enhanced by spectral subtraction.

Connected through hyperedge HE-1 (induced by source paper "robust sound event detection in bioacoustic sensor networks"):
  with members:
    - "learning to detect dysarthria from raw speech"
    - "attention based end to end models for small footprint keyword spotting"
    - "recognizing birds from sound the 2018 birdclef baseline system"
    - "convolutional recurrent neural networks for bird audio detection"
    - "densely connected cnns for bird audio detection"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "deformable convolutional networks"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"

Connected through hyperedge HE-2 (induced by source paper "batch normalized joint training for dnn based distant speech recognition"):
  with members:
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "theano a python framework for fast computation of mathematical expressions"
    - "recurrent batch normalization"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fitnets hints for thin deep nets"
    - "knowledge matters importance of prior information for optimization"

Connected through hyperedge HE-3 (induced by source paper "sound event detection using spatial features and convolutional recurrent neural network"):
  with members:
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "sound event detection in multichannel audio using spatial and harmonic features"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "audio surveillance a systematic review"

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"
    - "coherent to diffuse power ratio estimation for dereverberation"

Connected through hyperedge HE-5 (induced by source paper "an improved uncertainty decoding scheme with weighted samples for dnn hmm hybrid systems"):
  with members:
    - "coherent to diffuse power ratio estimation for dereverberation"

Connected through hyperedge HE-6 (induced by source paper "robust coherence based spectral enhancement for distant speech recognition"):
  with members:
    - "coherent to diffuse power ratio estimation for dereverberation"

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)