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: "toward socially infused information extraction embedding authors mentions and entities"
  Abstract: Entity linking is the task of identifying mentions of entities in text, and linking them to entries in a knowledge base. This task is especially difficult in microblogs, as there is little additional text to provide disambiguating context; rather, authors rely on an implicit common ground of shared knowledge with their readers. In this paper, we attempt to capture some of this implicit context by exploiting the social network structure in microblogs. We build on the theory of homophily, which implies that socially linked individuals share interests, and are therefore likely to mention the same sorts of entities. We implement this idea by encoding authors, mentions, and entities in a continuous vector space, which is constructed so that socially-connected authors have similar vector representations. These vectors are incorporated into a neural structured prediction model, which captures structural constraints that are inherent in the entity linking task. Together, these design decisions yield F1 improvements of 1%-5% on benchmark datasets, as compared to the previous state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "learning invariant representations of social media users"):
  with members:
    - "hunting for troll comments in news community forums"
    - "semeval 2019 task 6 identifying and categorizing offensive language in social media offenseval"
    - "deep learning is robust to massive label noise"
    - "arcface additive angular margin loss for deep face recognition"
    - "deep contextualized word representations"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "caveat emptor computational social science large scale missing data in a widely published reddit corpus"

Connected through hyperedge HE-2 (induced by source paper "you shall know a user by the company it keeps dynamic representations for social media users in nlp"):
  with members:
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "abusive language detection with graph convolutional networks"
    - "representing social media users for sarcasm detection"
    - "large scale crowdsourcing and characterization of twitter abusive behavior"
    - "cascade contextual sarcasm detection in online discussion forums"
    - "assessing state of the art sentiment models on state of the art sentiment datasets"
    - "attention is all you need"
    - "a decomposable attention model for natural language inference"

Connected through hyperedge HE-3 (induced by source paper "author profiling for hate speech detection"):
  with members:
    - "automated hate speech detection and the problem of offensive language"
    - "deep learning for hate speech detection in tweets"
    - "one step and two step classification for abusive language detection on twitter"
    - "deep learning for user comment moderation"
    - "utcnn a deep learning model of stance classificationon on social media text"
    - "node2vec scalable feature learning for networks"
    - "ex machina personal attacks seen at scale"
    - "modelling context with user embeddings for sarcasm detection in social media"

Connected through hyperedge HE-4 (induced by source paper "stylistic variation in social media part of speech tagging"):
  with members:
    - "finding function in form compositional character models for open vocabulary word representation"
    - "line large scale information network embedding"
    - "adam a method for stochastic optimization"
    - "user level sentiment analysis incorporating social networks"
    - "social networks that matter twitter under the microscope"

Connected through hyperedge HE-5 (induced by source paper "quantifying mental health from social media with neural user embeddings"):
  with members:
    - "modelling context with user embeddings for sarcasm detection in social media"
    - "learning multi faceted representations of individuals from heterogeneous evidence using neural networks"
    - "a primer on neural network models for natural language processing"
    - "distributed representations of sentences and documents"
    - "distributed representations of words and phrases and their compositionality"

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)