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: "task oriented learning of word embeddings for semantic relation classification"
  Abstract: We present a novel learning method for word embeddings designed for relation classification. Our word embeddings are trained by predicting words between noun pairs using lexical relation-specific features on a large unlabeled corpus. This allows us to explicitly incorporate relation-specific information into the word embeddings. The learned word embeddings are then used to construct feature vectors for a relation classification model. On a well-established semantic relation classification task, our method significantly outperforms a baseline based on a previously introduced word embedding method, and compares favorably to previous state-of-the-art models that use syntactic information or manually constructed external resources.

Connected through hyperedge HE-1 (induced by source paper "relational word embeddings"):
  with members:
    - "integrating multiplicative features into supervised distributional methods for lexical entailment"
    - "pair2vec compositional word pair embeddings for cross sentence inference"
    - "filling missing paths modeling co occurrences of word pairs and dependency paths for recognizing lexical semantic relations"
    - "neural latent relational analysis to capture lexical semantic relations in a vector space"
    - "hierarchical embeddings for hypernymy detection and directionality"
    - "specialising word vectors for lexical entailment"
    - "hyperlex a large scale evaluation of graded lexical entailment"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"

Connected through hyperedge HE-2 (induced by source paper "classifying medical relations in clinical text via convolutional neural networks"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "drug drug interaction extraction from biomedical text using long short term memory network"
    - "relation extraction from clinical texts using domain invariant convolutional neural network"
    - "improved relation classification by deep recurrent neural networks with data augmentation"
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "classifying relations by ranking with convolutional neural networks"
    - "local translation prediction with global sentence representation"
    - "classifying relations via long short term memory networks along shortest dependency path"

Connected through hyperedge HE-3 (induced by source paper "a comparison of word based and context based representations for classification problems in health informatics"):
  with members:
    - "assessing composition in sentence vector representations"
    - "deep contextualized word representations"
    - "universal sentence encoder"
    - "attention is all you need"
    - "the benefits of word embeddings features for active learning in clinical information extraction"
    - "are word embedding based features useful for sarcasm detection"
    - "representation learning for very short texts using weighted word embedding aggregation"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "stochastic learning of nonstationary kernels for natural language modeling"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "learning to skim text"
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "learning structural kernels for natural language processing"
    - "effective approaches to attention based neural machine translation"
    - "classifying relations by ranking with convolutional neural networks"
    - "classifying relations via long short term memory networks along shortest dependency path"
    - "semantic relation classification via convolutional neural networks with simple negative sampling"

Connected through hyperedge HE-5 (induced by source paper "end to end relation extraction using lstms on sequences and tree structures"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "bidirectional lstm crf models for sequence tagging"
    - "classifying relations by ranking with convolutional neural networks"
    - "classifying relations via long short term memory networks along shortest dependency path"
    - "semantic relation classification via convolutional neural networks with simple negative sampling"
    - "improved semantic representations from tree structured long short term memory networks"
    - "scheduled sampling for sequence prediction with recurrent neural networks"
    - "when are tree structures necessary for deep learning of representations"

Connected through hyperedge HE-6 (induced by source paper "discovering sound concepts and acoustic relations in text"):
  with members:
    - "weakly supervised scalable audio content analysis"
    - "audio event detection using weakly labeled data"
    - "eventnet a large scale structured concept library for complex event detection in video"
    - "classifying relations via long short term memory networks along shortest dependency path"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "improved relation extraction with feature rich compositional embedding models"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "classifying relations by ranking with convolutional neural networks"
    - "dependency based convolutional neural networks for sentence embedding"
    - "a dependency based neural network for relation classification"
    - "a semantic matching energy function for learning with multi relational data"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"

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)