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: "random forest dbscan for uspto inventor name disambiguation"
  Abstract: Name disambiguation and the subsequent name conflation are essential for the correct processing of person name queries in a digital library or other database. It distinguishes each unique person from all other records in the database. We study inventor name disambiguation for a patent database using methods and features from earlier work on author name disambiguation and propose a feature set appropriate for a patent database. A random forest was selected for the pairwise linking classifier since they outperform Naive Bayes, Logistic Regression, Support Vector Machines (SVM), Conditional Inference Tree, and Decision Trees. Blocking size, very important for scaling, was selected based on experiments that determined feature importance and accuracy. The DBSCAN algorithm is used for clustering records, using a distance function derived from random forest classifier. For additional scalability clustering was parallelized. Tests on the USPTO patent database show that our method successfully disambiguated 12 million inventor mentions within 6.5 hours. Evaluation on datasets from USPTO PatentsView inventor name disambiguation competition shows our algorithm outperforms all algorithms in the competition.

Connected through hyperedge HE-1 (induced by source paper "optimal transport based alignment of learned character representations for string similarity"):
  with members:
    - "gromov wasserstein alignment of word embedding spaces"
    - "attention is all you need"
    - "character level deep conflation for business data analytics"
    - "a decomposable attention model for natural language inference"
    - "match srnn modeling the recursive matching structure with spatial rnn"
    - "robust group linkage"
    - "a large annotated corpus for learning natural language inference"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "representing text as abstract images enables image classifiers to also simultaneously classify text"):
  with members:
    - "character level convolutional networks for text classification"
    - "disambiguation of patent inventors and assignees using high resolution geolocation data"
    - "stereo matching by training a convolutional neural network to compare image patches"
    - "abcnn attention based convolutional neural network for modeling sentence pairs"
    - "learning to compare image patches via convolutional neural networks"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-3 (induced by source paper "a novel text representation which enables image classifiers to perform text classification applied to name disambiguation"):
  with members:
    - "stereo matching by training a convolutional neural network to compare image patches"
    - "abcnn attention based convolutional neural network for modeling sentence pairs"
    - "learning to compare image patches via convolutional neural networks"
    - "caffe convolutional architecture for fast feature embedding"

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)