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: "cross language domain adaptation for classifying crisis related short messages"
  Abstract: Rapid crisis response requires real-time analysis of messages. After a disaster happens, volunteers attempt to classify tweets to determine needs, e.g., supplies, infrastructure damage, etc. Given labeled data, supervised machine learning can help classify these messages. Scarcity of labeled data causes poor performance in machine training. Can we reuse old tweets to train classifiers? How can we choose labeled tweets for training? Specifically, we study the usefulness of labeled data of past events. Do labeled tweets in different language help? We observe the performance of our classifiers trained using different combinations of training sets obtained from past disasters. We perform extensive experimentation on real crisis datasets and show that the past labels are useful when both source and target events are of the same type (e.g. both earthquakes). For similar languages (e.g., Italian and Spanish), cross-language domain adaptation was useful, however, when for different languages (e.g., Italian and English), the performance decreased.

Connected through hyperedge HE-1 (induced by source paper "mining the demographics of political sentiment from twitter using learning from label proportions"):
  with members:
    - "joint inference of multiple label types in large networks"
    - "learning about individuals from group statistics"

Connected through hyperedge HE-2 (induced by source paper "applications of online deep learning for crisis response using social media information"):
  with members:
    - "rapid classification of crisis related data on social networks using convolutional neural networks"
    - "twitter as a lifeline human annotated twitter corpora for nlp of crisis related messages"
    - "adam a method for stochastic optimization"
    - "processing social media messages in mass emergency a survey"
    - "a convolutional neural network for modelling sentences"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-3 (induced by source paper "rapid classification of crisis related data on social networks using convolutional neural networks"):
  with members:
    - "twitter as a lifeline human annotated twitter corpora for nlp of crisis related messages"
    - "bridging social media via distant supervision"
    - "processing social media messages in mass emergency a survey"
    - "a convolutional neural network for modelling sentences"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "adadelta an adaptive learning rate method"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-4 (induced by source paper "semi supervised discovery of informative tweets during the emerging disasters"):
  with members:
    - "processing social media messages in mass emergency a survey"

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)