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: "analyzing hidden representations in end to end automatic speech recognition systems"
  Abstract: Neural models have become ubiquitous in automatic speech recognition systems. While neural networks are typically used as acoustic models in more complex systems, recent studies have explored end-to-end speech recognition systems based on neural networks, which can be trained to directly predict text from input acoustic features. Although such systems are conceptually elegant and simpler than traditional systems, it is less obvious how to interpret the trained models. In this work, we analyze the speech representations learned by a deep end-to-end model that is based on convolutional and recurrent layers, and trained with a connectionist temporal classification (CTC) loss. We use a pre-trained model to generate frame-level features which are given to a classifier that is trained on frame classification into phones. We evaluate representations from different layers of the deep model and compare their quality for predicting phone labels. Our experiments shed light on important aspects of the end-to-end model such as layer depth, model complexity, and other design choices.

Connected through hyperedge HE-1 (induced by source paper "untangling in invariant speech recognition"):
  with members:
    - "analyzing learned representations of a deep asr performance prediction model"
    - "gate activation signal analysis for gated recurrent neural networks and its correlation with phoneme boundaries"
    - "deep speaker an end to end neural speaker embedding system"
    - "deep neural networks with random gaussian weights a universal classification strategy"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "geodesics of learned representations"
    - "convergent learning do different neural networks learn the same representations"

Connected through hyperedge HE-2 (induced by source paper "improving oov detection and resolution with external language models in acoustic to word asr"):
  with members:
    - "advancing acoustic to word ctc model"
    - "english conversational telephone speech recognition by humans and machines"
    - "building competitive direct acoustics to word models for english conversational speech recognition"
    - "direct acoustics to word models for english conversational speech recognition"
    - "acoustic to word model without oov"
    - "comparing human and machine errors in conversational speech transcription"
    - "towards better decoding and language model integration in sequence to sequence models"
    - "tying word vectors and word classifiers a loss framework for language modeling"

Connected through hyperedge HE-3 (induced by source paper "designing and interpreting probes with control tasks"):
  with members:
    - "linspector multilingual probing tasks for word representations"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "probing what different nlp tasks teach machines about function word comprehension"
    - "linguistic knowledge and transferability of contextual representations"
    - "under the hood using diagnostic classifiers to investigate and improve how language models track agreement information"
    - "analyzing learned representations of a deep asr performance prediction model"
    - "assessing composition in sentence vector representations"
    - "language modeling teaches you more syntax than translation does lessons learned through auxiliary task analysis"

Connected through hyperedge HE-4 (induced by source paper "what should do can lstms learn when parsing auxiliary verb constructions"):
  with members:
    - "recursive subtree composition in lstm based dependency parsing"
    - "studying the inductive biases of rnns with synthetic variations of natural languages"
    - "can lstm learn to capture agreement the case of basque"
    - "deep contextualized word representations"
    - "analysis methods in neural language processing a survey"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deep rnns encode soft hierarchical syntax"
    - "colorless green recurrent networks dream hierarchically"

Connected through hyperedge HE-5 (induced by source paper "analyzing asr pretraining for low resource speech to text translation"):
  with members:
    - "fluent translations from disfluent speech in end to end speech translation"
    - "massively multilingual adversarial speech recognition"
    - "tied multitask learning for neural speech translation"
    - "multilingual sequence to sequence speech recognition architecture transfer learning and language modeling"
    - "low resource speech to text translation"
    - "end to end automatic speech translation of audiobooks"
    - "pre training on high resource speech recognition improves low resource speech to text translation"
    - "sequence based multi lingual low resource speech recognition"

Connected through hyperedge HE-6 (induced by source paper "analyzing phonetic and graphemic representations in end to end automatic speech recognition"):
  with members:
    - "towards end to end code switching speech recognition"
    - "analyzing learned representations of a deep asr performance prediction model"
    - "stream attention based multi array end to end speech recognition"
    - "phonetic and graphemic systems for multi genre broadcast transcription"
    - "analysis methods in neural language processing a survey"
    - "learning word like units from joint audio visual analysis"
    - "gate activation signal analysis for gated recurrent neural networks and its correlation with phoneme boundaries"
    - "visualisation and diagnostic classifiers reveal how recurrent and recursive neural networks process hierarchical structure"

Connected through hyperedge HE-7 (induced by source paper "contextual phonetic pretraining for end to end utterance level language and speaker recognition"):
  with members:
    - "audio linguistic embeddings for spoken sentences"
    - "analyzing deep cnn based utterance embeddings for acoustic model adaptation"
    - "speaker embedding extraction with phonetic information"
    - "deep contextualized word representations"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "voxceleb2 deep speaker recognition"
    - "attention is all you need"
    - "deep speaker an end to end neural speaker embedding system"

Connected through hyperedge HE-8 (induced by source paper "agi safety literature review"):
  with members:
    - "categorizing variants of goodhart s law"
    - "psychlab a psychology laboratory for deep reinforcement learning agents"
    - "rainbow combining improvements in deep reinforcement learning"
    - "ai safety gridworlds"
    - "a causal framework for explaining the predictions of black box sequence to sequence models"
    - "when will ai exceed human performance evidence from ai experts"
    - "deep tamer interactive agent shaping in high dimensional state spaces"
    - "should robots be obedient"

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)