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: "human as real time sensors of social and physical events a case study of twitter and sports games"
  Abstract: In this work, we study how Twitter can be used as a sensor to detect frequent and diverse social and physical events in real-time. We devise efficient data collection and event recognition solutions that work despite various limits on free access to Twitter data. We describe a web service implementation of our solution and report our experience with the 2010-2011 US National Football League (NFL) games. The service was able to recognize NFL game events within 40 seconds and with accuracy up to 90%. This capability will be very useful for not only real-time electronic program guide for live broadcast programs but also refined auction of advertisement slots. More importantly, it demonstrates for the first time the feasibility of using Twitter for real-time social and physical event detection for ubiquitous computing.

Connected through hyperedge HE-1 (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"
    - "sub story detection in twitter with hierarchical dirichlet processes"
    - "convolutional neural networks for sentence classification"
    - "towards real time summarization of scheduled events from twitter streams"

Connected through hyperedge HE-2 (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"
    - "event detection tracking and visualization in twitter a mention anomaly based approach"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-3 (induced by source paper "adaptive polling in hierarchical social networks using blackwell dominance"):
  with members:
    - "what do your friends think efficient polling methods for networks using friendship paradox"
    - "coarse graining and the blackwell order"
    - "140 characters to victory using twitter to predict the uk 2015 general election"
    - "online reputation and polling systems data incest social learning and revealed preferences"
    - "active sequential hypothesis testing"

Connected through hyperedge HE-4 (induced by source paper "learning to discover key moments in social media streams"):
  with members:
    - "dynamical classes of collective attention in twitter"

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)