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: "exploring the effectiveness of convolutional neural networks for answer selection in end to end question answering"
  Abstract: Most work on natural language question answering today focuses on answer selection: given a candidate list of sentences, determine which contains the answer. Although important, answer selection is only one stage in a standard end-to-end question answering pipeline. This paper explores the effectiveness of convolutional neural networks (CNNs) for answer selection in an end-to-end context using the standard TrecQA dataset. We observe that a simple idf-weighted word overlap algorithm forms a very strong baseline, and that despite substantial efforts by the community in applying deep learning to tackle answer selection, the gains are modest at best on this dataset. Furthermore, it is unclear if a CNN is more effective than the baseline in an end-to-end context based on standard retrieval metrics. To further explore this finding, we conducted a manual user evaluation, which confirms that answers from the CNN are detectably better than those from idf-weighted word overlap. This result suggests that users are sensitive to relatively small differences in answer selection quality.

Connected through hyperedge HE-1 (induced by source paper "bas an answer selection method using bert language model"):
  with members:
    - "a compare aggregate model with latent clustering for answer selection"
    - "attention based pairwise multi perspective convolutional neural network for answer selection in question answering"
    - "albert a lite bert for self supervised learning of language representations"
    - "deep learning using rectified linear units relu"
    - "anmm ranking short answer texts with attention based neural matching model"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "recurrent additive networks"
    - "applying deep learning to answer selection a study and an open task"

Connected through hyperedge HE-2 (induced by source paper "attention based pairwise multi perspective convolutional neural network for answer selection in question answering"):
  with members:
    - "anmm ranking short answer texts with attention based neural matching model"
    - "hyperbolic representation learning for fast and efficient neural question answering"
    - "attention is all you need"
    - "learning to rank question answer pairs with holographic dual lstm architecture"
    - "applying deep learning to answer selection a study and an open task"
    - "deep learning for answer sentence selection"
    - "convolutional neural networks for sentence classification"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-3 (induced by source paper "multi perspective relevance matching with hierarchical convnets for social media search"):
  with members:
    - "end to end neural ad hoc ranking with kernel pooling"
    - "visual relationship detection with internal and external linguistic knowledge distillation"
    - "talking to your tv context aware voice search with hierarchical recurrent neural networks"
    - "pacrr a position aware neural ir model for relevance matching"
    - "generating holistic 3d scene abstractions for text based image retrieval"
    - "a deep relevance matching model for ad hoc retrieval"
    - "learning to match using local and distributed representations of text for web search"
    - "text matching as image recognition"

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)