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: "answer sequence learning with neural networks for answer selection in community question answering"
  Abstract: In this paper, the answer selection problem in community question answering (CQA) is regarded as an answer sequence labeling task, and a novel approach is proposed based on the recurrent architecture for this problem. Our approach applies convolution neural networks (CNNs) to learning the joint representation of question-answer pair firstly, and then uses the joint representation as input of the long short-term memory (LSTM) to learn the answer sequence of a question for labeling the matching quality of each answer. Experiments conducted on the SemEval 2015 CQA dataset shows the effectiveness of our approach.

Connected through hyperedge HE-1 (induced by source paper "a joint model for multimodal document quality assessment"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "machine translation evaluation meets community question answering"
    - "a dataset of peer reviews peerread collection insights and nlp applications"
    - "deep paper gestalt"
    - "automatic academic paper rating based on modularized hierarchical convolutional neural network"
    - "skipflow incorporating neural coherence features for end to end automatic text scoring"
    - "automatic text scoring using neural networks"
    - "xception deep learning with depthwise separable convolutions"

Connected through hyperedge HE-2 (induced by source paper "semeval 2017 task 3 community question answering"):
  with members:
    - "hunting for troll comments in news community forums"
    - "semeval 2015 task 3 answer selection in community question answering"
    - "semeval 2016 task 3 community question answering"
    - "global thread level inference for comment classification in community question answering"
    - "semanticz at semeval 2016 task 3 ranking relevant answers in community question answering using semantic similarity based on fine tuned word embeddings"
    - "pairwise neural machine translation evaluation"
    - "machine translation evaluation meets community question answering"
    - "a decomposable attention model for natural language inference"

Connected through hyperedge HE-3 (induced by source paper "semeval 2016 task 3 community question answering"):
  with members:
    - "hunting for troll comments in news community forums"
    - "semeval 2015 task 3 answer selection in community question answering"
    - "global thread level inference for comment classification in community question answering"
    - "semanticz at semeval 2016 task 3 ranking relevant answers in community question answering using semantic similarity based on fine tuned word embeddings"
    - "pairwise neural machine translation evaluation"
    - "machine translation evaluation meets community question answering"
    - "uh prhlt at semeval 2016 task 3 combining lexical and semantic based features for community question answering"
    - "query chains learning to rank from implicit feedback"

Connected through hyperedge HE-4 (induced by source paper "global thread level inference for comment classification in community question answering"):
  with members:
    - "semeval 2015 task 3 answer selection in community question answering"
    - "a sentimental education sentiment analysis using subjectivity summarization based on minimum cuts"

Connected through hyperedge HE-5 (induced by source paper "joint multitask learning for community question answering using task specific embeddings"):
  with members:
    - "semeval 2016 task 3 community question answering"
    - "semanticz at semeval 2016 task 3 ranking relevant answers in community question answering using semantic similarity based on fine tuned word embeddings"
    - "pairwise neural machine translation evaluation"
    - "adversarial domain adaptation for duplicate question detection"
    - "fact checking in community forums"
    - "large scale goodness polarity lexicons for community question answering"
    - "machine translation evaluation with neural networks"
    - "semi supervised question retrieval with gated convolutions"

Connected through hyperedge HE-6 (induced by source paper "lcsts a large scale chinese short text summarization dataset"):
  with members:
    - "a hierarchical neural autoencoder for paragraphs and documents"
    - "convolutional neural network architectures for matching natural language sentences"
    - "gated feedback recurrent neural networks"
    - "neural responding machine for short text conversation"
    - "abstractive multi document summarization via phrase selection and merging"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"

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)