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: "event detection tracking and visualization in twitter a mention anomaly based approach"
  Abstract: The ever-growing number of people using Twitter makes it a valuable source of timely information. However, detecting events in Twitter is a difficult task, because tweets that report interesting events are overwhelmed by a large volume of tweets on unrelated topics. Existing methods focus on the textual content of tweets and ignore the social aspect of Twitter. In this paper, we propose mention-anomaly-based event detection (MABED), a novel statistical method that relies solely on tweets and leverages the creation frequency of dynamic links (i.e., mentions) that users insert in tweets to detect significant events and estimate the magnitude of their impact over the crowd. MABED also differs from the literature in that it dynamically estimates the period of time during which each event is discussed, rather than assuming a predefined fixed duration for all events. The experiments we conducted on both English and French Twitter data show that the mention-anomaly-based approach leads to more accurate event detection and improved robustness in presence of noisy Twitter content. Qualitatively speaking, we find that MABED helps with the interpretation of detected events by providing clear textual descriptions and precise temporal descriptions. We also show how MABED can help understanding users’ interest. Furthermore, we describe three visualizations designed to favor an efficient exploration of the detected events.

Connected through hyperedge HE-1 (induced by source paper "conversational structure aware and context sensitive topic model for online discussions"):
  with members:
    - "context aware image annotation in active learning"
    - "eliminating search intent bias in learning to rank"
    - "knowledge guided text structuring in clinical trials"
    - "improving topic models with latent feature word representations"
    - "a joint model of conversational discourse and latent topics on microblogs"
    - "jointly learning word embeddings and latent topics"
    - "probabilistic latent semantic analysis"

Connected through hyperedge HE-2 (induced by source paper "real time event detection on social data streams"):
  with members:
    - "event detection with neural networks a rigorous empirical evaluation"
    - "named entity sequence classification"
    - "objective criteria for the evaluation of clustering methods"

Connected through hyperedge HE-3 (induced by source paper "smerc social media event response clustering using textual and temporal information"):
  with members:
    - "pachinko prediction a bayesian method for event prediction from social media data"
    - "real time detection of content polluters in partially observable twitter networks"
    - "on clustering time series using euclidean distance and pearson correlation"
    - "tweet2vec character based distributed representations for social media"
    - "a data driven model for influenza transmission incorporating media effects"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "efficient estimation of word representations in vector space"
    - "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 "benchmarking top k keyword and top k document processing with t2k2 and t2k2d2"):
  with members:
    - "a scalable document based architecture for text analysis"
    - "benchmarking sentiment analysis methods for large scale texts a case for using continuum scored words and word shift graphs"
    - "bigdatabench a big data benchmark suite from internet services"
    - "primeball a parallel processing framework benchmark for big data applications in the cloud"

Connected through hyperedge HE-5 (induced by source paper "t 2 k 2 the twitter top k keywords benchmark"):
  with members:
    - "a scalable document based architecture for text analysis"
    - "lshtc a benchmark for large scale text classification"
    - "benchmarking sentiment analysis methods for large scale texts a case for using continuum scored words and word shift graphs"
    - "data processing benchmarks"
    - "primeball a parallel processing framework benchmark for big data applications in the cloud"

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.SI(Social and Information Networks)