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: "building a web scale dependency parsed corpus from commoncrawl"
  Abstract: We present DepCC, the largest-to-date linguistically analyzed corpus in English including 365 million documents, composed of 252 billion tokens and 7.5 billion of named entity occurrences in 14.3 billion sentences from a web-scale crawl of the \textsc{Common Crawl} project. The sentences are processed with a dependency parser and with a named entity tagger and contain provenance information, enabling various applications ranging from training syntax-based word embeddings to open information extraction and question answering. We built an index of all sentences and their linguistic meta-data enabling quick search across the corpus. We demonstrate the utility of this corpus on the verb similarity task by showing that a distributional model trained on our corpus yields better results than models trained on smaller corpora, like Wikipedia. This distributional model outperforms the state of art models of verb similarity trained on smaller corpora on the SimVerb3500 dataset.

Connected through hyperedge HE-1 (induced by source paper "the two pass softmax algorithm"):
  with members:
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"
    - "in datacenter performance analysis of a tensor processing unit"
    - "high performance ultra low precision convolutions on mobile devices"
    - "efficient softmax approximation for gpus"
    - "eie efficient inference engine on compressed deep neural network"
    - "faster cnns with direct sparse convolutions and guided pruning"
    - "exploring the limits of language modeling"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-2 (induced by source paper "efficient contextual representation learning without softmax layer"):
  with members:
    - "von mises fisher loss for training sequence to sequence models with continuous outputs"
    - "multilingual constituency parsing with self attention and pre training"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "higher order coreference resolution with coarse to fine inference"
    - "an analysis of neural language modeling at multiple scales"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dissecting contextual word embeddings architecture and representation"

Connected through hyperedge HE-3 (induced by source paper "answering comparative questions better than ten blue links"):
  with members:
    - "categorization of comparative sentences for argument mining"
    - "what is the essence of a claim cross domain claim identification"
    - "social media argumentation mining the quest for deliberateness in raucousness"
    - "understanding and detecting supporting arguments of diverse types"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-4 (induced by source paper "categorizing comparative sentences"):
  with members:
    - "answering comparative questions better than ten blue links"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "what is the essence of a claim cross domain claim identification"
    - "social media argumentation mining the quest for deliberateness in raucousness"
    - "xgboost a scalable tree boosting system"
    - "learning distributed representations of sentences from unlabelled data"

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)