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: "metapad meta pattern discovery from massive text corpora"
  Abstract: Mining textual patterns in news, tweets, papers, and many other kinds of text corpora has been an active theme in text mining and NLP research. Previous studies adopt a dependency parsing-based pattern discovery approach. However, the parsing results lose rich context around entities in the patterns, and the process is costly for a corpus of large scale. In this study, we propose a novel typed textual pattern structure, called meta pattern, which is extended to a frequent, informative, and precise subsequence pattern in certain context. We propose an efficient framework, called MetaPAD, which discovers meta patterns from massive corpora with three techniques: (1) it develops a context-aware segmentation method to carefully determine the boundaries of patterns with a learnt pattern quality assessment function, which avoids costly dependency parsing and generates high-quality patterns; (2) it identifies and groups synonymous meta patterns from multiple facets---their types, contexts, and extractions; and (3) it examines type distributions of entities in the instances extracted by each group of patterns, and looks for appropriate type levels to make discovered patterns precise. Experiments demonstrate that our proposed framework discovers high-quality typed textual patterns efficiently from different genres of massive corpora and facilitates information extraction.

Connected through hyperedge HE-1 (induced by source paper "taxoexpan self supervised taxonomy expansion with position enhanced graph neural network"):
  with members:
    - "a user centered concept mining system for query and document understanding at tencent"
    - "every child should have parents a taxonomy refinement algorithm based on hyperbolic term embeddings"
    - "position aware graph neural networks"
    - "setexpan corpus based set expansion via context feature selection and rank ensemble"
    - "hiexpan task guided taxonomy construction by hierarchical tree expansion"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hierarchical graph representation learning with differentiable pooling"

Connected through hyperedge HE-2 (induced by source paper "relation learning on social networks with multi modal graph edge variational autoencoders"):
  with members:
    - "i know you ll be back interpretable new user clustering and churn prediction on a mobile social application"
    - "similarity modeling on heterogeneous networks via automatic path discovery"
    - "neural embedding propagation on heterogeneous networks"
    - "cubenet multi facet hierarchical heterogeneous network construction analysis and mining"
    - "representation learning on graphs with jumping knowledge networks"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "relationship profiling over social networks reverse smoothness from similarity to closeness"
    - "topological recurrent neural network for diffusion prediction"

Connected through hyperedge HE-3 (induced by source paper "nero a neural rule grounding framework for label efficient relation extraction"):
  with members:
    - "learning dual retrieval module for semi supervised relation extraction"
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "attention guided graph convolutional networks for relation extraction"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "joint bootstrapping machines for high confidence relation extraction"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "weakly supervised relation extraction by pattern enhanced embedding learning"

Connected through hyperedge HE-4 (induced by source paper "neural rule grounding for low resource relation extraction"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "generalize symbolic knowledge with neural rule engine"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "joint bootstrapping machines for high confidence relation extraction"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "harnessing deep neural networks with logic rules"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-5 (induced by source paper "predicting drug responses by propagating interactions through text enhanced drug gene networks"):
  with members:
    - "automated phrase mining from massive text corpora"
    - "meta path guided embedding for similarity search in large scale heterogeneous information networks"
    - "image captioning with semantic attention"
    - "self critical sequence training for image captioning"
    - "a critical review of recurrent neural networks for sequence learning"
    - "language models for image captioning the quirks and what works"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-6 (induced by source paper "hiexpan task guided taxonomy construction by hierarchical tree expansion"):
  with members:
    - "setexpan corpus based set expansion via context feature selection and rank ensemble"
    - "end to end reinforcement learning for automatic taxonomy induction"
    - "taxonomy induction using hypernym subsequences"
    - "weakly supervised relation extraction by pattern enhanced embedding learning"
    - "automated phrase mining from massive text corpora"
    - "pte predictive text embedding through large scale heterogeneous text networks"
    - "line large scale information network embedding"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "diag nre a neural pattern diagnosis framework for distantly supervised neural relation extraction"):
  with members:
    - "robust distant supervision relation extraction via deep reinforcement learning"
    - "reinforcement learning for relation classification from noisy data"
    - "neural open information extraction"
    - "learning with noise enhance distantly supervised relation extraction with dynamic transition matrix"
    - "snorkel rapid training data creation with weak supervision"
    - "heterogeneous supervision for relation extraction a representation learning approach"
    - "learning the structure of generative models without labeled data"
    - "socratic learning augmenting generative models to incorporate latent subsets in training data"

Connected through hyperedge HE-8 (induced by source paper "taxogen unsupervised topic taxonomy construction by adaptive term embedding and clustering"):
  with members:
    - "expert finding in heterogeneous bibliographic networks with locally trained embeddings"
    - "inriasac simple hypernym extraction methods"
    - "distributed representations of words and phrases and their compositionality"

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)