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: "a bert baseline for the natural questions"
  Abstract: This technical note describes a new baseline for the Natural Questions. Our model is based on BERT and reduces the gap between the model F1 scores reported in the original dataset paper and the human upper bound by 30% and 50% relative for the long and short answer tasks respectively. This baseline has been submitted to the official NQ leaderboard at this http URL. Code, preprocessed data and pretrained model are available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "tydi qa a benchmark for information seeking question answering in typologically diverse languages"):
  with members:
    - "neural arabic question answering"
    - "boolq exploring the surprising difficulty of natural yes no questions"
    - "latent retrieval for weakly supervised open domain question answering"
    - "what question answering can learn from trivia nerds"
    - "question answering is a format when is it useful"
    - "korquad1 0 korean qa dataset for machine reading comprehension"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "mlqa evaluating cross lingual extractive question answering"

Connected through hyperedge HE-2 (induced by source paper "on identifiability in transformers"):
  with members:
    - "attention is not not explanation"
    - "are sixteen heads really better than one"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "attention is not all you need for commonsense reasoning"
    - "visualizing and measuring the geometry of bert"
    - "bert rediscovers the classical nlp pipeline"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "adding interpretable attention to neural translation models improves word alignment"

Connected through hyperedge HE-3 (induced by source paper "a discrete hard em approach for weakly supervised question answering"):
  with members:
    - "latent retrieval for weakly supervised open domain question answering"
    - "learning to generalize from sparse and underspecified rewards"
    - "a comprehensive exploration on wikisql with table aware word contextualization"
    - "compositional questions do not necessitate multi hop reasoning"
    - "multi style generative reading comprehension"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "x sql reinforce schema representation with context"
    - "multiqa an empirical investigation of generalization and transfer in reading comprehension"

Connected through hyperedge HE-4 (induced by source paper "an exploration of data augmentation and sampling techniques for domain agnostic question answering"):
  with members:
    - "learning and evaluating general linguistic intelligence"
    - "transformer xl attentive language models beyond a fixed length context"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "multiqa an empirical investigation of generalization and transfer in reading comprehension"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "duorc towards complex language understanding with paraphrased reading comprehension"

Connected through hyperedge HE-5 (induced by source paper "frustratingly easy natural question answering"):
  with members:
    - "span selection pre training for question answering"
    - "synthetic qa corpora generation with roundtrip consistency"
    - "sg net syntax guided machine reading comprehension"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "a qualitative comparison of coqa squad 2 0 and quac"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"

Connected through hyperedge HE-6 (induced by source paper "meta answering for machine reading"):
  with members:
    - "latent retrieval for weakly supervised open domain question answering"
    - "answering while summarizing multi task learning for multi hop qa with evidence extraction"
    - "synthetic qa corpora generation with roundtrip consistency"
    - "revealing the importance of semantic retrieval for machine reading at scale"
    - "a geometric perspective on optimal representations for reinforcement learning"
    - "probing neural network comprehension of natural language arguments"
    - "episodic memory reader learning what to remember for question answering from streaming data"
    - "roberta a robustly optimized bert pretraining approach"

Connected through hyperedge HE-7 (induced by source paper "knowledge guided text retrieval and reading for open domain question answering"):
  with members:
    - "latent retrieval for weakly supervised open domain question answering"
    - "pullnet open domain question answering with iterative retrieval on knowledge bases and text"
    - "compositional questions do not necessitate multi hop reasoning"
    - "a discrete hard em approach for weakly supervised question answering"
    - "multi passage bert a globally normalized bert model for open domain question answering"
    - "multi step entity centric information retrieval for multi hop question answering"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "dynamic integration of background knowledge in neural nlu systems"

Connected through hyperedge HE-8 (induced by source paper "multi granularity representations of dialog"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "pretraining methods for dialog context representation learning"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"
    - "attention is all you need"

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)