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: "attention based neural text segmentation"
  Abstract: Text segmentation plays an important role in various Natural Language Processing (NLP) tasks like summarization, context understanding, document indexing and document noise removal. Previous methods for this task require manual feature engineering, huge memory requirements and large execution times. To the best of our knowledge, this paper is the first one to present a novel supervised neural approach for text segmentation. Specifically, we propose an attention-based bidirectional LSTM model where sentence embeddings are learned using CNNs and the segments are predicted based on contextual information. This model can automatically handle variable sized context information. Compared to the existing competitive baselines, the proposed model shows a performance improvement of ~7% in WinDiff score on three benchmark datasets.

Connected through hyperedge HE-1 (induced by source paper "cawa an attention network for credit attribution"):
  with members:
    - "text segmentation on multilabel documents a distant supervised approach"
    - "text segmentation as a supervised learning task"
    - "multiple instance learning networks for fine grained sentiment analysis"
    - "rationalizing neural predictions"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "advances in domain independent linear text segmentation"

Connected through hyperedge HE-2 (induced by source paper "logical segmentation of source code"):
  with members:
    - "a language agnostic model for semantic source code labeling"
    - "automated vulnerability detection in source code using deep representation learning"
    - "learning to repair software vulnerabilities with generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "deeptilebars visualizing term distribution for neural information retrieval"):
  with members:
    - "modeling diverse relevance patterns in ad hoc retrieval"
    - "deeprank a new deep architecture for relevance ranking in information retrieval"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "learning visual features from snapshots for web search"
    - "pacrr a position aware neural ir model for relevance matching"
    - "dsrim a deep neural information retrieval model enhanced by a knowledge resource driven representation of documents"
    - "a deep relevance matching model for ad hoc retrieval"
    - "a study of matchpyramid models on ad hoc retrieval"

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.LG(Machine Learning)