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: "fera 2017 addressing head pose in the third facial expression recognition and analysis challenge"
  Abstract: The field of Automatic Facial Expression Analysis has grown rapidly in recent years. However, despite progress in new approaches as well as benchmarking efforts, most evaluations still focus on either posed expressions, near-frontal recordings, or both. This makes it hard to tell how existing expression recognition approaches perform under conditions where faces appear in a wide range of poses (or camera views), displaying ecologically valid expressions. The main obstacle for assessing this is the availability of suitable data, and the challenge proposed here addresses this limitation. The FG 2017 Facial Expression Recognition and Analysis challenge (FERA 2017) extends FERA 2015 to the estimation of Action Units occurrence and intensity under different camera views. In this paper we present the third challenge in automatic recognition of facial expressions, to be held in conjunction with the 12th IEEE conference on Face and Gesture Recognition, May 2017, in Washington, United States. Two sub-challenges are defined: the detection of AU occurrence, and the estimation of AU intensity. In this work we outline the evaluation protocol, the data used, and the results of a baseline method for both sub-challenges.

Connected through hyperedge HE-1 (induced by source paper "jaa net joint facial action unit detection and face alignment via adaptive attention"):
  with members:
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "deep structure inference network for facial action unit recognition"
    - "deep adaptive attention for joint facial action unit detection and face alignment"
    - "facial action unit detection using attention and relation learning"
    - "constrained joint cascade regression framework for simultaneous facial action unit recognition and facial landmark detection"
    - "simultaneous facial landmark detection pose and deformation estimation under facial occlusion"
    - "hand keypoint detection in single images using multiview bootstrapping"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"

Connected through hyperedge HE-2 (induced by source paper "face behavior a la carte expressions affect and action units in a single network"):
  with members:
    - "expression affect action unit recognition aff wild2 multi task learning and arcface"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "a multi task learning generation framework valence arousal action units primary expressions"
    - "aff wild2 extending the aff wild database for affect recognition"
    - "emotiw 2018 audio video student engagement and group level affect prediction"
    - "two level attention with two stage multi task learning for facial emotion recognition"
    - "emotionet challenge recognition of facial expressions of emotion in the wild"

Connected through hyperedge HE-3 (induced by source paper "expression affect action unit recognition aff wild2 multi task learning and arcface"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "training deep neural networks with different datasets in the wild the emotion recognition paradigm"
    - "arcface additive angular margin loss for deep face recognition"
    - "local learning with deep and handcrafted features for facial expression recognition"
    - "a multi task learning generation framework valence arousal action units primary expressions"
    - "aff wild2 extending the aff wild database for affect recognition"
    - "sphereface deep hypersphere embedding for face recognition"

Connected through hyperedge HE-4 (induced by source paper "multi level attention network using text audio and video for depression prediction"):
  with members:
    - "inverse boosting pruning trees for depression detection on twitter"
    - "the verbal and non verbal signals of depression combining acoustics text and visuals for estimating depression level"
    - "universal sentence encoder"
    - "attention is all you need"
    - "avec 2016 depression mood and emotion recognition workshop and challenge"
    - "openxbow introducing the passau open source crossmodal bag of words toolkit"

Connected through hyperedge HE-5 (induced by source paper "facial action unit detection using attention and relation learning"):
  with members:
    - "deep structure inference network for facial action unit recognition"
    - "deep adaptive attention for joint facial action unit detection and face alignment"
    - "joint action unit localisation and intensity estimation through heatmap regression"
    - "deep structured learning for facial action unit intensity estimation"
    - "constrained joint cascade regression framework for simultaneous facial action unit recognition and facial landmark detection"
    - "picanet learning pixel wise contextual attention for saliency detection"
    - "eac net a region based deep enhancing and cropping approach for facial action unit detection"
    - "deepcoder semi parametric variational autoencoders for automatic facial action coding"

Connected through hyperedge HE-6 (induced by source paper "the many moods of emotion"):
  with members:
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "deep video portraits"
    - "an occam s razor view on learning audiovisual emotion recognition with small training sets"
    - "covariance pooling for facial expression recognition"
    - "ganimation anatomically aware facial animation from a single image"
    - "cake compact and accurate k dimensional representation of emotion"
    - "convolutional neural networks pretrained on large face recognition datasets for emotion classification from video"

Connected through hyperedge HE-7 (induced by source paper "an occam s razor view on learning audiovisual emotion recognition with small training sets"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "covariance pooling for facial expression recognition"
    - "emotiw 2018 audio video student engagement and group level affect prediction"
    - "emotionet challenge recognition of facial expressions of emotion in the wild"
    - "learnable pooling with context gating for video classification"
    - "temporal multimodal fusion for video emotion classification in the wild"
    - "improved regularization of convolutional neural networks with cutout"
    - "soundnet learning sound representations from unlabeled video"

Connected through hyperedge HE-8 (induced by source paper "facial expression analysis under partial occlusion a survey"):
  with members:
    - "towards reading hidden emotions a comparative study of spontaneous micro expression spotting and recognition methods"
    - "emotionet challenge recognition of facial expressions of emotion in the wild"
    - "avec 2016 depression mood and emotion recognition workshop and challenge"
    - "confidence weighted local expression predictions for occlusion handling in expression recognition and action unit detection"
    - "face expression recognition and analysis the state of the art"

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)