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: "the ibm 2015 english conversational telephone speech recognition system"
  Abstract: We describe the latest improvements to the IBM English conversational telephone speech recognition system. Some of the techniques that were found beneficial are: maxout networks with annealed dropout rates; networks with a very large number of outputs trained on 2000 hours of data; joint modeling of partially unfolded recurrent neural networks and convolutional nets by combining the bottleneck and output layers and retraining the resulting model; and lastly, sophisticated language model rescoring with exponential and neural network LMs. These techniques result in an 8.0% word error rate on the Switchboard part of the Hub5-2000 evaluation test set which is 23% relative better than our previous best published result.

Connected through hyperedge HE-1 (induced by source paper "adversarial learning of deepfakes in accounting"):
  with members:
    - "detection of accounting anomalies in the latent space using adversarial autoencoder neural networks"
    - "text based editing of talking head video"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "voice conversion with conditional samplernn"
    - "adversarial text generation via feature mover s distance"
    - "adversarial attacks and defences a survey"
    - "rsgan face swapping and editing using face and hair representation in latent spaces"
    - "everybody dance now"

Connected through hyperedge HE-2 (induced by source paper "deep minimax probability machine"):
  with members:
    - "sparse dnns with improved adversarial robustness"
    - "compressing recurrent neural networks with tensor ring for action recognition"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"
    - "extending defensive distillation"
    - "learning compact recurrent neural networks with block term tensor decomposition"
    - "end to end learning for self driving cars"
    - "universal adversarial perturbations"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-3 (induced by source paper "one size does not fit all quantifying and exposing the accuracy latency trade off in machine learning cloud service apis via tolerance tiers"):
  with members:
    - "branchynet fast inference via early exiting from deep neural networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "clipper a low latency online prediction serving system"
    - "the microsoft 2016 conversational speech recognition system"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "conditional deep learning for energy efficient and enhanced pattern recognition"

Connected through hyperedge HE-4 (induced by source paper "sndcnn self normalizing deep cnns with scaled exponential linear units for speech recognition"):
  with members:
    - "self normalizing neural networks"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "the microsoft 2017 conversational speech recognition system"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "densely connected convolutional networks"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-5 (induced by source paper "a comparison of online automatic speech recognition systems and the nonverbal responses to unintelligible speech"):
  with members:
    - "english conversational telephone speech recognition by humans and machines"
    - "the microsoft 2017 conversational speech recognition system"
    - "state of the art speech recognition with sequence to sequence models"
    - "achieving human parity in conversational speech recognition"

Connected through hyperedge HE-6 (induced by source paper "adaptive adversarial attack on scene text recognition"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "adversarial vision challenge"
    - "adversarial vulnerability for any classifier"
    - "audio adversarial examples targeted attacks on speech to text"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial attacks on neural network policies"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "pixeldefend leveraging generative models to understand and defend against adversarial examples"

Connected through hyperedge HE-7 (induced by source paper "the marchex 2018 english conversational telephone speech recognition system"):
  with members:
    - "the capio 2017 conversational speech recognition system"
    - "english conversational telephone speech recognition by humans and machines"
    - "the microsoft 2017 conversational speech recognition system"
    - "semi supervised model training for unbounded conversational speech recognition"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "the ibm 2016 english conversational telephone speech recognition system"
    - "deep speech 2 end to end speech recognition in english and mandarin"

Connected through hyperedge HE-8 (induced by source paper "empirical evaluation of speaker adaptation on dnn based acoustic model"):
  with members:
    - "the microsoft 2016 conversational speech recognition system"
    - "fast and accurate recurrent neural network acoustic models for speech recognition"
    - "feedforward sequential memory networks a new structure to learn long term dependency"
    - "highway long short term memory rnns for distant speech recognition"
    - "long short term memory based recurrent neural network architectures for large vocabulary speech recognition"

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)