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: "heterogeneous knowledge transfer in video emotion recognition attribution and summarization"
  Abstract: Emotion is a key element in user-generated video. However, it is difficult to understand emotions conveyed in such videos due to the complex and unstructured nature of user-generated content and the sparsity of video frames expressing emotion. In this paper, for the first time, we propose a technique for transferring knowledge from heterogeneous external sources, including image and textual data, to facilitate three related tasks in understanding video emotion: emotion recognition, emotion attribution and emotion-oriented summarization. Specifically, our framework (1) learns a video encoding from an auxiliary emotional image dataset in order to improve supervised video emotion recognition, and (2) transfers knowledge from an auxiliary textual corpora for zero-shot recognition of emotion classes unseen during training. The proposed technique for knowledge transfer facilitates novel applications of emotion attribution and emotion-oriented summarization. A comprehensive set of experiments on multiple datasets demonstrate the effectiveness of our framework.

Connected through hyperedge HE-1 (induced by source paper "learning to augment expressions for few shot fine grained facial expression recognition"):
  with members:
    - "deep learning for human affect recognition insights and new developments"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "deep emotion facial expression recognition using attentional convolutional network"
    - "improving person re identification by attribute and identity learning"
    - "deep facial expression recognition a survey"
    - "local learning with deep and handcrafted features for facial expression recognition"
    - "low shot learning from imaginary data"
    - "vgan based image representation learning for privacy preserving facial expression recognition"

Connected through hyperedge HE-2 (induced by source paper "an end to end visual audio attention network for emotion recognition in user generated videos"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "affective computing for large scale heterogeneous multimedia data a survey"
    - "pdanet polarity consistent deep attention network for fine grained visual emotion regression"
    - "human centered emotion recognition in animated gifs"
    - "deep learning for sentiment analysis a survey"
    - "a multi task neural approach for emotion attribution classification and summarization"
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "cbam convolutional block attention module"

Connected through hyperedge HE-3 (induced by source paper "neural sequential phrase grounding seqground"):
  with members:
    - "iterative visual reasoning beyond convolutions"
    - "a neural multi sequence alignment technique neumatch"
    - "graph r cnn for scene graph generation"
    - "interpretable and globally optimal prediction for textual grounding using image concepts"
    - "mattnet modular attention network for referring expression comprehension"
    - "discriminative bimodal networks for visual localization and detection with natural language queries"
    - "query guided regression network with context policy for phrase grounding"
    - "learning two branch neural networks for image text matching tasks"

Connected through hyperedge HE-4 (induced by source paper "affective computing for large scale heterogeneous multimedia data a survey"):
  with members:
    - "pdanet polarity consistent deep attention network for fine grained visual emotion regression"
    - "polysemous visual semantic embedding for cross modal retrieval"
    - "meld a multimodal multi party dataset for emotion recognition in conversations"
    - "multi attention recurrent network for human communication comprehension"
    - "a data driven approach to mid level perceptual musical feature modeling"
    - "depechemood a bilingual emotion lexicon built through simple yet powerful techniques"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "looking beyond a clever narrative visual context and attention are primary drivers of affect in video advertisements"

Connected through hyperedge HE-5 (induced by source paper "mobile video action recognition"):
  with members:
    - "spatiotemporal pyramid network for video action recognition"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "videocapsulenet a simplified network for action detection"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "face focused cross stream network for deception detection in videos"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "compressed video action recognition"

Connected through hyperedge HE-6 (induced by source paper "the liar s walk detecting deception with gait and gesture"):
  with members:
    - "anomaly detection in video sequence with appearance motion correspondence"
    - "learning regularity in skeleton trajectories for anomaly detection in videos"
    - "context aware emotion recognition networks"
    - "step spatial temporal graph convolutional networks for emotion perception from gaits"
    - "gait recognition via disentangled representation learning"
    - "identifying emotions from walking using affective and deep features"
    - "pose guided human video generation"
    - "real world anomaly detection in surveillance videos"

Connected through hyperedge HE-7 (induced by source paper "a fine grained facial expression database for end to end multi pose facial expression recognition"):
  with members:
    - "deep emotion facial expression recognition using attentional convolutional network"
    - "local learning with deep and handcrafted features for facial expression recognition"
    - "hydraplus net attentive deep features for pedestrian analysis"
    - "pose normalized image generation for person re identification"
    - "going deeper in facial expression recognition using deep neural networks"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "do deep neural networks learn facial action units when doing expression recognition"
    - "a light cnn for deep face representation with noisy labels"

Connected through hyperedge HE-8 (induced by source paper "zero shot object detection"):
  with members:
    - "zero shot detection"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "a unified approach for conventional zero shot generalized zero shot and few shot learning"
    - "semantic autoencoder for zero shot learning"
    - "discriminative bimodal networks for visual localization and detection with natural language queries"
    - "recent advances in zero shot recognition"
    - "visually aligned word embeddings for improving zero shot learning"
    - "expanded parts model for semantic description of humans in still images"

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)