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: "predicting personal traits from facial images using convolutional neural networks augmented with facial landmark information"
  Abstract: We consider the task of predicting various traits of a person given an image of their face. We estimate both objective traits, such as gender, ethnicity and hair-color; as well as subjective traits, such as the emotion a person expresses or whether he is humorous or attractive. For sizeable experimentation, we contribute a new Face Attributes Dataset (FAD), having roughly 200,000 attribute labels for the above traits, for over 10,000 facial images. Due to the recent surge of research on Deep Convolutional Neural Networks (CNNs), we begin by using a CNN architecture for estimating facial attributes and show that they indeed provide an impressive baseline performance. To further improve performance, we propose a novel approach that incorporates facial landmark information for input images as an additional channel, helping the CNN learn better attribute-specific features so that the landmarks across various training images hold correspondence. We empirically analyse the performance of our method, showing consistent improvement over the baseline across traits.

Connected through hyperedge HE-1 (induced by source paper "an attention mechanism for answer selection using a combined global and local view"):
  with members:
    - "generating high quality and informative conversation responses with sequence to sequence models"
    - "squad 100 000 questions for machine comprehension of text"
    - "a neural attention model for abstractive sentence summarization"
    - "applying deep learning to answer selection a study and an open task"
    - "effective approaches to attention based neural machine translation"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "towards ai complete question answering a set of prerequisite toy tasks"
    - "reasoning about entailment with neural attention"

Connected through hyperedge HE-2 (induced by source paper "predicting first impressions with deep learning"):
  with members:
    - "face attribute prediction using off the shelf cnn features"
    - "walk and learn facial attribute representation learning from egocentric video and contextual data"
    - "moon a mixed objective optimization network for the recognition of facial attributes"
    - "very deep convolutional networks for large scale image recognition"
    - "deep learning face attributes in the wild"
    - "panda pose aligned networks for deep attribute modeling"

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.CV(Computer Vision and Pattern Recognition)