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: "6 seconds of sound and vision creativity in micro videos"
  Abstract: The notion of creativity, as opposed to related concepts such as beauty or interestingness, has not been studied from the perspective of automatic analysis of multimedia content. Meanwhile, short online videos shared on social media platforms, or micro-videos, have arisen as a new medium for creative expression. In this paper we study creative micro-videos in an effort to understand the features that make a video creative, and to address the problem of automatic detection of creative content. Defining creative videos as those that are novel and have aesthetic value, we conduct a crowdsourcing experiment to create a dataset of over 3, 800 micro-videos labelled as creative and non-creative. We propose a set of computational features that we map to the components of our definition of creativity, and conduct an analysis to determine which of these features correlate most with creative video. Finally, we evaluate a supervised approach to automatically detect creative video, with promising results, showing that it is necessary to model both aesthetic value and novelty to achieve optimal classification accuracy.

Connected through hyperedge HE-1 (induced by source paper "data driven analysis of tiny touchscreen performance with microjam"):
  with members:
    - "how constraints affect content the case of twitter s switch from 140 to 280 characters"
    - "robojam a musical mixture density network for collaborative touchscreen interaction"

Connected through hyperedge HE-2 (induced by source paper "equal but not the same understanding the implicit relationship between persuasive images and text"):
  with members:
    - "context aware captions from context agnostic supervision"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "self supervised learning of visual features through embedding images into text topic spaces"
    - "advise symbolism and external knowledge for decoding advertisements"
    - "linking image and text with 2 way nets"
    - "captioning images with diverse objects"
    - "densecap fully convolutional localization networks for dense captioning"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "and now for something completely different visual novelty in an online network of designers"):
  with members:
    - "bam the behance artistic media dataset for recognition beyond photography"
    - "why do men get more attention exploring factors behind success in an online design community"
    - "an image is worth more than a thousand favorites surfacing the hidden beauty of flickr pictures"
    - "quantifying creativity in art networks"
    - "going deeper with convolutions"
    - "an extended cencov campbell characterization of conditional information geometry"

Connected through hyperedge HE-4 (induced by source paper "beautiful and damned combined effect of content quality and social ties on user engagement"):
  with members:
    - "to click or not to click automatic selection of beautiful thumbnails from videos"
    - "online actions with offline impact how online social networks influence online and offline user behavior"
    - "the majority illusion in social networks"
    - "photo aesthetics ranking network with attributes and content adaptation"
    - "yfcc100m the new data in multimedia research"
    - "an image is worth more than a thousand favorites surfacing the hidden beauty of flickr pictures"
    - "the beauty of capturing faces rating the quality of digital portraits"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-5 (induced by source paper "protest activity detection and perceived violence estimation from social media images"):
  with members:
    - "greysanatomy vs yankees demographics and hashtag use on twitter"
    - "deciphering the 2016 u s presidential campaign in the twitter sphere a comparison of the trumpists and clintonists"
    - "deep residual learning for image recognition"
    - "characterizing the demographics behind the blacklivesmatter movement"
    - "deep learning face attributes in the wild"
    - "big questions for social media big data representativeness validity and other methodological pitfalls"

Connected through hyperedge HE-6 (induced by source paper "video2gif automatic generation of animated gifs from video"):
  with members:
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "unsupervised learning of visual representations using videos"
    - "unsupervised extraction of video highlights via robust recurrent auto encoders"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "learning fine grained image similarity with deep ranking"
    - "deep convolutional ranking for multilabel image annotation"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "the open world of micro videos"):
  with members:
    - "eventnet a large scale structured concept library for complex event detection in video"
    - "predicting important objects for egocentric video summarization"
    - "multi scale recognition with dag cnns"
    - "very deep convolutional networks for large scale image recognition"
    - "a discriminative cnn video representation for event detection"
    - "two stream convolutional networks for action recognition in videos"
    - "ucf101 a dataset of 101 human actions classes from videos in the wild"

Connected through hyperedge HE-8 (induced by source paper "to click or not to click automatic selection of beautiful thumbnails from videos"):
  with members:
    - "video2gif automatic generation of animated gifs from video"
    - "the beauty of capturing faces rating the quality of digital portraits"
    - "imagenet large scale visual recognition challenge"

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.MM(Multimedia)