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: "a semantics based measure of emoji similarity"
  Abstract: Emoji have grown to become one of the most important forms of communication on the web. With its widespread use, measuring the similarity of emoji has become an important problem for contemporary text processing since it lies at the heart of sentiment analysis, search, and interface design tasks. This paper presents a comprehensive analysis of the semantic similarity of emoji through embedding models that are learned over machine-readable emoji meanings in the EmojiNet knowledge base. Using emoji descriptions, emoji sense labels and emoji sense definitions, and with different training corpora obtained from Twitter and Google News, we develop and test multiple embedding models to measure emoji similarity. To evaluate our work, we create a new dataset called EmoSim508, which assigns human-annotated semantic similarity scores to a set of 508 carefully selected emoji pairs. After validation with EmoSim508, we present a real-world use-case of our emoji embedding models using a sentiment analysis task and show that our models outperform the previous best-performing emoji embedding model on this task. The EmoSim508 dataset and our emoji embedding models are publicly released with this paper and can be downloaded from http://emojinet.knoesis.org/.

Connected through hyperedge HE-1 (induced by source paper "multimodal emotion classification"):
  with members:
    - "what s ur type contextualized classification of user types in marijuana related communications using compositional multiview embedding"
    - "which emoji talks best for my picture"
    - "learning emoji embeddings using emoji co occurrence network graph"
    - "multimodal emoji prediction"
    - "fasttext zip compressing text classification models"
    - "emojinet an open service and api for emoji sense discovery"
    - "finding street gang members on twitter"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-2 (induced by source paper "which emoji talks best for my picture"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "the new modality emoji challenges in prediction anticipation and retrieval"
    - "what s ur type contextualized classification of user types in marijuana related communications using compositional multiview embedding"
    - "learning emoji embeddings using emoji co occurrence network graph"
    - "multimodal emoji prediction"
    - "advances in pre training distributed word representations"
    - "are emojis predictable"
    - "fasttext zip compressing text classification models"

Connected through hyperedge HE-3 (induced by source paper "multimodal emoji prediction"):
  with members:
    - "the new modality emoji challenges in prediction anticipation and retrieval"
    - "exploring emoji usage and prediction through a temporal variation lens"
    - "through a gender lens learning usage patterns of emojis from large scale android users"
    - "spice up your chat the intentions and sentiment effects of using emoji"
    - "are emojis predictable"
    - "emojinet an open service and api for emoji sense discovery"
    - "emoji2vec learning emoji representations from their description"
    - "finding function in form compositional character models for open vocabulary word representation"

Connected through hyperedge HE-4 (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"
    - "sentiment perception of readers and writers in emoji use"
    - "emojinet building a machine readable sense inventory for emoji"
    - "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-5 (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"
    - "advances in pre training distributed word representations"
    - "emojinet an open service and api for emoji sense discovery"
    - "emojinet building a machine readable sense inventory for emoji"
    - "retrofitting word vectors to semantic lexicons"

Connected through hyperedge HE-6 (induced by source paper "exploring emoji usage and prediction through a temporal variation lens"):
  with members:
    - "the new modality emoji challenges in prediction anticipation and retrieval"
    - "multimodal emoji prediction"
    - "spice up your chat the intentions and sentiment effects of using emoji"
    - "are emojis predictable"
    - "emojinet an open service and api for emoji sense discovery"
    - "emoji2vec learning emoji representations from their description"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "knowledge will propel machine understanding of content extrapolating from current examples"):
  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"
    - "emojinet building a machine readable sense inventory for emoji"
    - "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)