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: "semi supervised clustering for short text via deep representation learning"
  Abstract: In this work, we propose a semi-supervised method for short text clustering, where we represent texts as distributed vectors with neural networks, and use a small amount of labeled data to specify our intention for clustering. We design a novel objective to combine the representation learning process and the k-means clustering process together, and optimize the objective with both labeled data and unlabeled data iteratively until convergence through three steps: (1) assign each short text to its nearest centroid based on its representation from the current neural networks; (2) re-estimate the cluster centroids based on cluster assignments from step (1); (3) update neural networks according to the objective by keeping centroids and cluster assignments fixed. Experimental results on four datasets show that our method works significantly better than several other text clustering methods.

Connected through hyperedge HE-1 (induced by source paper "discovering new intents via constrained deep adaptive clustering with cluster refinement"):
  with members:
    - "deep unknown intent detection with margin loss"
    - "train one get one free partially supervised neural network for bug report duplicate detection and clustering"
    - "huggingface s transformers state of the art natural language processing"
    - "deep clustering for unsupervised learning of visual features"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "prototypical networks for few shot learning"
    - "learning to cluster in order to transfer across domains and tasks"
    - "towards k means friendly spaces simultaneous deep learning and clustering"

Connected through hyperedge HE-2 (induced by source paper "a deep learning based approach for measuring the domain similarity of persian texts"):
  with members:
    - "supervised learning of universal sentence representations from natural language inference data"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"
    - "comparative study of cnn and rnn for natural language processing"
    - "enriching word vectors with subword information"
    - "convolutional neural networks for sentence classification"
    - "a convolutional neural network for modelling sentences"
    - "efficient estimation of word representations in vector space"
    - "recurrent convolutional neural networks for discourse compositionality"

Connected through hyperedge HE-3 (induced by source paper "convexification of neural graph"):
  with members:
    - "ndt neual decision tree towards fully functioned neural graph"
    - "hungarian layer logics empowered neural architecture"
    - "bilateral multi perspective matching for natural language sentences"
    - "identity mappings in deep residual networks"
    - "lstm based deep learning models for non factoid answer selection"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-4 (induced by source paper "multi perspective neural architecture for recommendation system"):
  with members:
    - "semi supervised qa with generative domain adaptive nets"
    - "deep learning in neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "bilateral multi perspective matching for natural language sentences"):
  with members:
    - "attentive pooling networks"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "long short term memory networks for machine reading"
    - "a decomposable attention model for natural language inference"
    - "sentence similarity learning by lexical decomposition and composition"
    - "enhanced lstm for natural language inference"
    - "modelling interaction of sentence pair with coupled lstms"
    - "learning natural language inference using bidirectional lstm model and inner attention"

Connected through hyperedge HE-6 (induced by source paper "hungarian layer logics empowered neural architecture"):
  with members:
    - "efficient neural architecture search via parameter sharing"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "bilateral multi perspective matching for natural language sentences"
    - "pointing the unknown words"
    - "sentence similarity learning by lexical decomposition and composition"
    - "dynamic neural turing machine with soft and hard addressing schemes"
    - "faq based question answering via word alignment"
    - "neural variational inference for text processing"

Connected through hyperedge HE-7 (induced by source paper "k nearest neighbor augmented neural networks for text classification"):
  with members:
    - "bilateral multi perspective matching for natural language sentences"
    - "sentence similarity learning by lexical decomposition and composition"
    - "very deep convolutional networks for text classification"
    - "matching networks for one shot learning"
    - "multi perspective context matching for machine comprehension"
    - "bag of tricks for efficient text classification"
    - "efficient character level document classification by combining convolution and recurrent layers"
    - "adam a method for stochastic optimization"

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)