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: "uh prhlt at semeval 2016 task 3 combining lexical and semantic based features for community question answering"
  Abstract: In this work we describe the system built for the three English subtasks of the SemEval 2016 Task 3 by the Department of Computer Science of the University of Houston (UH) and the Pattern Recognition and Human Language Technology (PRHLT) research center - Universitat Polit`ecnica de Val`encia: UH-PRHLT. Our system represents instances by using both lexical and semantic-based similarity measures between text pairs. Our semantic features include the use of distributed representations of words, knowledge graphs generated with the BabelNet multilingual semantic network, and the FrameNet lexical database. Experimental results outperform the random and Google search engine baselines in the three English subtasks. Our approach obtained the highest results of subtask B compared to the other task participants.

Connected through hyperedge HE-1 (induced by source paper "semeval 2016 task 3 community question answering"):
  with members:
    - "hunting for troll comments in news community forums"
    - "semeval 2015 task 3 answer selection in community question answering"
    - "global thread level inference for comment classification in community question answering"
    - "semanticz at semeval 2016 task 3 ranking relevant answers in community question answering using semantic similarity based on fine tuned word embeddings"
    - "pairwise neural machine translation evaluation"
    - "machine translation evaluation meets community question answering"
    - "answer sequence learning with neural networks for answer selection in community question answering"
    - "query chains learning to rank from implicit feedback"

Connected through hyperedge HE-2 (induced by source paper "query expansion for cross language question re ranking"):
  with members:
    - "semeval 2016 task 3 community question answering"
    - "unsupervised cross lingual information retrieval using monolingual data only"
    - "query expansion with locally trained word embeddings"

Connected through hyperedge HE-3 (induced by source paper "adversarial training for community question answer selection based on multi scale matching"):
  with members:
    - "semeval 2016 task 3 community question answering"
    - "semanticz at semeval 2016 task 3 ranking relevant answers in community question answering using semantic similarity based on fine tuned word embeddings"
    - "irgan a minimax game for unifying generative and discriminative information retrieval models"
    - "bilateral multi perspective matching for natural language sentences"
    - "deep reinforcement learning for dialogue generation"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "a decomposable attention model for natural language inference"
    - "a compare aggregate model for matching text sequences"

Connected through hyperedge HE-4 (induced by source paper "an unsupervised model with attention autoencoders for question retrieval"):
  with members:
    - "semeval 2015 task 3 answer selection in community question answering"
    - "semeval 2016 task 3 community question answering"
    - "semeval 2017 task 3 community question answering"
    - "attention is all you need"
    - "a hierarchical neural autoencoder for paragraphs and documents"
    - "semi supervised sequence learning"
    - "adam a method for stochastic optimization"
    - "sequence to sequence learning with neural networks"

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)