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: "sub story detection in twitter with hierarchical dirichlet processes"
  Abstract: Social media has now become the de facto information source on real world events. The challenge, however, due to the high volume and velocity nature of social media streams, is in how to follow all posts pertaining to a given event over time, a task referred to as story detection. Moreover, there are often several different stories pertaining to a given event, which we refer to as sub-stories and the corresponding task of their automatic detection as sub-story detection. This paper proposes hierarchical Dirichlet processes (HDP), a probabilistic topic model, as an effective method for automatic sub-story detection. HDP can learn sub-topics associated with sub-stories which enables it to handle subtle variations in sub-stories. It is compared with state- of-the-art story detection approaches based on locality sensitive hashing and spectral clustering. We demonstrate the superior performance of HDP for sub-story detection on real world Twitter data sets using various evaluation measures. The ability of HDP to learn sub-topics helps it to recall the sub- stories with high precision. Another contribution of this paper is in demonstrating that the conversational structures within the Twitter stream can be used to improve sub-story detection performance significantly.

Connected through hyperedge HE-1 (induced by source paper "modeling islamist extremist communications on social media using contextual dimensions religion ideology and hate"):
  with members:
    - "predictive analysis on twitter techniques and applications"
    - "the rise of jihadist propaganda on social networks"
    - "domain adapted word embeddings for improved sentiment classification"
    - "what s ur type contextualized classification of user types in marijuana related communications using compositional multiview embedding"
    - "probabilistic fasttext for multi sense word embeddings"
    - "automated hate speech detection and the problem of offensive language"
    - "predicting online extremism content adopters and interaction reciprocity"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-2 (induced by source paper "unsupervised detection of sub events in large scale disasters"):
  with members:
    - "sub event detection from twitter streams as a sequence labeling problem"
    - "graph based semi supervised learning with convolution neural networks to classify crisis related tweets"
    - "empathi an ontology for emergency managing and planning about hazard crisis"
    - "savitr a system for real time location extraction from microblogs during emergencies"
    - "domain adaptation with adversarial training and graph embeddings"
    - "fasttext zip compressing text classification models"
    - "intelligent disaster response via social media analysis a survey"
    - "rapid classification of crisis related data on social networks using convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "sub event detection from twitter streams as a sequence labeling problem"):
  with members:
    - "joint entity recognition and relation extraction as a multi head selection problem"
    - "adversarial training for multi context joint entity and relation extraction"
    - "global normalization of convolutional neural networks for joint entity and relation classification"
    - "an attentive neural architecture for joint segmentation and parsing and its application to real estate ads"
    - "convolutional neural networks for sentence classification"
    - "towards real time summarization of scheduled events from twitter streams"
    - "human as real time sensors of social and physical events a case study of twitter and sports games"

Connected through hyperedge HE-4 (induced by source paper "discourse aware rumour stance classification in social media using sequential classifiers"):
  with members:
    - "detection and resolution of rumours in social media a survey"
    - "supporting the use of user generated content in journalistic practice"
    - "semeval 2017 task 8 rumoureval determining rumour veracity and support for rumours"
    - "turing at semeval 2017 task 8 sequential approach to rumour stance classification with branch lstm"
    - "using gaussian processes for rumour stance classification in social media"
    - "stance detection with bidirectional conditional encoding"
    - "stance classification in rumours as a sequential task exploiting the tree structure of social media conversations"
    - "learning reporting dynamics during breaking news for rumour detection in social media"

Connected through hyperedge HE-5 (induced by source paper "latent dirichlet allocation lda and topic modeling models applications a survey"):
  with members:
    - "improving topic models with latent feature word representations"
    - "catching fire via likes inferring topic preferences of trump followers on twitter"
    - "fast online em for big topic modeling"
    - "unveiling the political agenda of the european parliament plenary a topical analysis"
    - "et lda joint topic modeling for aligning events and their twitter feedback"
    - "expectation propogation for the generative aspect model"
    - "the author topic model for authors and documents"
    - "modeling online reviews with multi grain topic models"

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.IR(Information Retrieval)