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: "a context aware attention network for interactive question answering"
  Abstract: Neural network based sequence-to-sequence models in an encoder-decoder framework have been successfully applied to solve Question Answering (QA) problems, predicting answers from statements and questions. However, almost all previous models have failed to consider detailed context information and unknown states under which systems do not have enough information to answer given questions. These scenarios with incomplete or ambiguous information are very common in the setting of Interactive Question Answering (IQA). To address this challenge, we develop a novel model, employing context-dependent word-level attention for more accurate statement representations and question-guided sentence-level attention for better context modeling. We also generate unique IQA datasets to test our model, which will be made publicly available. Employing these attention mechanisms, our model accurately understands when it can output an answer or when it requires generating a supplementary question for additional input depending on different contexts. When available, user's feedback is encoded and directly applied to update sentence-level attention to infer an answer. Extensive experiments on QA and IQA datasets quantitatively demonstrate the effectiveness of our model with significant improvement over state-of-the-art conventional QA models.

Connected through hyperedge HE-1 (induced by source paper "look before you hop conversational question answering over knowledge graphs using judicious context expansion"):
  with members:
    - "answering complex questions by joining multi document evidence with quasi knowledge graphs"
    - "interpretation of natural language rules in conversational machine reading"
    - "comqa a community sourced dataset for complex factoid question answering with paraphrase clusters"
    - "the web as a knowledge base for answering complex questions"
    - "coqa a conversational question answering challenge"
    - "open domain question answering using early fusion of knowledge bases and text"
    - "complex sequential question answering towards learning to converse over linked question answer pairs with a knowledge graph"
    - "learning to paraphrase for question answering"

Connected through hyperedge HE-2 (induced by source paper "r vqa learning visual relation facts with semantic attention for visual question answering"):
  with members:
    - "vip cnn visual phrase guided convolutional neural network"
    - "co attending free form regions and detections with multi modal multiplicative feature embedding for visual question answering"
    - "mutan multimodal tucker fusion for visual question answering"
    - "incorporating external knowledge to answer open domain visual questions with dynamic memory networks"
    - "deep variation structured reinforcement learning for visual relationship and attribute detection"
    - "hadamard product for low rank bilinear pooling"
    - "image captioning with semantic attention"
    - "a focused dynamic attention model for visual question answering"

Connected through hyperedge HE-3 (induced by source paper "interactive semantic parsing for if then recipes via hierarchical reinforcement learning"):
  with members:
    - "multimodal hierarchical reinforcement learning policy for task oriented visual dialog"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "deep dyna q integrating planning for task completion dialogue policy learning"
    - "neural approaches to conversational ai"
    - "video captioning via hierarchical reinforcement learning"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "learning a neural semantic parser from user feedback"

Connected through hyperedge HE-4 (induced by source paper "a hierarchical attention model for social contextual image recommendation"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "nais neural attentive item similarity model for recommendation"
    - "neural collaborative filtering"
    - "a survey on network embedding"
    - "related pins at pinterest the evolution of a real world recommender system"
    - "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks"
    - "autosvd an efficient hybrid collaborative filtering model via contractive auto encoders"
    - "vista a visually socially and temporally aware model for artistic recommendation"

Connected through hyperedge HE-5 (induced by source paper "repeatnet a repeat aware neural recommendation machine for session based recommendation"):
  with members:
    - "adversarial personalized ranking for recommendation"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "aspect aware latent factor model rating prediction with ratings and reviews"
    - "nais neural attentive item similarity model for recommendation"
    - "neural attentive session based recommendation"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "neural machine translation by jointly learning to align and translate"
    - "using temporal data for making recommendations"

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)