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: "emojinet building a machine readable sense inventory for emoji"
  Abstract: Emoji are a contemporary and extremely popular way to enhance electronic communication. Without rigid semantics attached to them, emoji symbols take on different meanings based on the context of a message. Thus, like the word sense disambiguation task in natural language processing, machines also need to disambiguate the meaning or sense of an emoji. In a first step toward achieving this goal, this paper presents EmojiNet, the first machine readable sense inventory for emoji. EmojiNet is a resource enabling systems to link emoji with their context-specific meaning. It is automatically constructed by integrating multiple emoji resources with BabelNet, which is the most comprehensive multilingual sense inventory available to date. The paper discusses its construction, evaluates the automatic resource creation process, and presents a use case where EmojiNet disambiguates emoji usage in tweets. EmojiNet is available online for use at this http URL.

Connected through hyperedge HE-1 (induced by source paper "i stand with you using emojis to study solidarity in crisis events"):
  with members:
    - "spice up your chat the intentions and sentiment effects of using emoji"
    - "emoticons vs emojis on twitter a causal inference approach"
    - "sentiment of emojis"

Connected through hyperedge HE-2 (induced by source paper "the new modality emoji challenges in prediction anticipation and retrieval"):
  with members:
    - "through a gender lens learning usage patterns of emojis from large scale android users"
    - "a semantics based measure of emoji similarity"
    - "sentiment perception of readers and writers in emoji use"
    - "the imagenet shuffle reorganized pre training for video event detection"
    - "yfcc100m the new data in multimedia research"
    - "the new data and new challenges in multimedia research"
    - "going deeper with convolutions"
    - "zero shot learning by convex combination of semantic embeddings"

Connected through hyperedge HE-3 (induced by source paper "what s ur type contextualized classification of user types in marijuana related communications using compositional multiview embedding"):
  with members:
    - "predictive analysis on twitter techniques and applications"
    - "semantic structure and interpretability of word embeddings"
    - "verse versatile graph embeddings from similarity measures"
    - "attributed social network embedding"
    - "a semantics based measure of emoji similarity"
    - "advances in pre training distributed word representations"
    - "emojinet an open service and api for emoji sense discovery"
    - "retrofitting word vectors to semantic lexicons"

Connected through hyperedge HE-4 (induced by source paper "mining the relationship between emoji usage patterns and personality"):
  with members:
    - "spice up your chat the intentions and sentiment effects of using emoji"
    - "are emojis predictable"

Connected through hyperedge HE-5 (induced by source paper "a semantics based measure of emoji similarity"):
  with members:
    - "emojinet an open service and api for emoji sense discovery"
    - "finding street gang members on twitter"
    - "word embeddings to enhance twitter gang member profile identification"
    - "emoji2vec learning emoji representations from their description"
    - "siamese cbow optimizing word embeddings for sentence representations"
    - "sentiment of emojis"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-6 (induced by source paper "emojinet an open service and api for emoji sense discovery"):
  with members:
    - "finding street gang members on twitter"
    - "emoji2vec learning emoji representations from their description"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "spice up your chat the intentions and sentiment effects of using emoji"):
  with members:
    - "emoji2vec learning emoji representations from their description"
    - "emoticons vs emojis on twitter a causal inference approach"

Connected through hyperedge HE-8 (induced by source paper "knowledge will propel machine understanding of content extrapolating from current examples"):
  with members:
    - "a semantics based measure of emoji similarity"
    - "emojinet an open service and api for emoji sense discovery"
    - "finding street gang members on twitter"
    - "word embeddings to enhance twitter gang member profile identification"
    - "implicit entity linking in tweets"
    - "a hybrid approach to finding relevant social media content for complex domain specific information needs"

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)