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: "moliere automatic biomedical hypothesis generation system"
  Abstract: Hypothesis generation is becoming a crucial time-saving technique which allows biomedical researchers to quickly discover implicit connections between important concepts. Typically, these systems operate on domain-specific fractions of public medical data. MOLIERE, in contrast, utilizes information from over 24.5 million documents. At the heart of our approach lies a multi-modal and multi-relational network of biomedical objects extracted from several heterogeneous datasets from the National Center for Biotechnology Information (NCBI). These objects include but are not limited to scientific papers, keywords, genes, proteins, diseases, and diagnoses. We model hypotheses using Latent Dirichlet Allocation applied on abstracts found near shortest paths discovered within this network, and demonstrate the effectiveness of MOLIERE by performing hypothesis generation on historical data. Our network, implementation, and resulting data are all publicly available for the broad scientific community.

Connected through hyperedge HE-1 (induced by source paper "cbag conditional biomedical abstract generation"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "transfer learning in biomedical natural language processing an evaluation of bert and elmo on ten benchmarking datasets"
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "language models as knowledge bases"
    - "the deepfake detection challenge dfdc preview dataset"
    - "ernie enhanced representation through knowledge integration"
    - "ctrl a conditional transformer language model for controllable generation"
    - "reducing bert pre training time from 3 days to 76 minutes"

Connected through hyperedge HE-2 (induced by source paper "navigation based candidate expansion and pretrained language models for citation recommendation"):
  with members:
    - "transfer learning in biomedical natural language processing an evaluation of bert and elmo on ten benchmarking datasets"
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "passage re ranking with bert"
    - "a scalable hybrid research paper recommender system for microsoft academic"
    - "scibert a pretrained language model for scientific text"
    - "scibert pretrained contextualized embeddings for scientific text"
    - "roberta a robustly optimized bert pretraining approach"
    - "multi hop knowledge graph reasoning with reward shaping"

Connected through hyperedge HE-3 (induced by source paper "agatha automatic graph mining and transformer based hypothesis generation approach"):
  with members:
    - "scibert a pretrained language model for scientific text"
    - "scibert pretrained contextualized embeddings for scientific text"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "scispacy fast and robust models for biomedical natural language processing"
    - "pytorch biggraph a large scale graph embedding system"
    - "large scale validation of hypothesis generation systems via candidate ranking"
    - "are abstracts enough for hypothesis generation"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "planalyzer assessing threats to the validity of online experiments"):
  with members:
    - "designing and deploying online field experiments"
    - "surveyman programming and automatically debugging surveys"
    - "church a language for generative models"
    - "ologs a categorical framework for knowledge representation"

Connected through hyperedge HE-5 (induced by source paper "are abstracts enough for hypothesis generation"):
  with members:
    - "validation and topic driven ranking for biomedical hypothesis generation systems"
    - "large scale validation of hypothesis generation systems via candidate ranking"
    - "automated phrase mining from massive text corpora"
    - "fasttext zip compressing text classification models"
    - "bag of tricks for efficient text classification"
    - "scalable topical phrase mining from text corpora"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "validation and topic driven ranking for biomedical hypothesis generation systems"):
  with members:
    - "are abstracts enough for hypothesis generation"
    - "efficient correlated topic modeling with topic embedding"
    - "fasttext zip compressing text classification models"
    - "scalable topical phrase mining from text corpora"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-7 (induced by source paper "large scale validation of hypothesis generation systems via candidate ranking"):
  with members:
    - "are abstracts enough for hypothesis generation"
    - "fasttext zip compressing text classification models"
    - "scalable topical phrase mining from text corpora"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-8 (induced by source paper "scalable dynamic topic modeling with clustered latent dirichlet allocation clda"):
  with members:
    - "bibliographic analysis with the citation network topic model"
    - "peacock learning long tail topic features for industrial applications"
    - "dependent hierarchical normalized random measures for dynamic topic modeling"
    - "timeline a dynamic hierarchical dirichlet process model for recovering birth death and evolution of topics in text stream"

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.IR(Information Retrieval)