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: "memory based shallow parsing"
  Abstract: We present a memory-based learning (MBL) approach to shallow parsing in which POS tagging, chunking, and identification of syntactic relations are formulated as memory-based modules. The experiments reported in this paper show competitive results, the F-value for the Wall Street Journal (WSJ) treebank is: 93.8% for NP chunking, 94.7% for VP chunking, 77.1% for subject detection and 79.0% for object detection.

Connected through hyperedge HE-1 (induced by source paper "parsing coordination for spoken language understanding"):
  with members:
    - "double embeddings and cnn based sequence labeling for aspect extraction"
    - "deep contextualized word representations"
    - "constituency parsing with a self attentive encoder"
    - "adversarial neural machine translation"
    - "attention is all you need"
    - "adversarial learning for neural dialogue generation"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "improving sentence compression by learning to predict gaze"

Connected through hyperedge HE-2 (induced by source paper "state of the art evaluation and recommendations regarding document processing and visualization techniques"):
  with members:
    - "machine learning in automated text categorization"
    - "a classification approach to word prediction"

Connected through hyperedge HE-3 (induced by source paper "combining a self organising map with memory based learning"):
  with members:
    - "forgetting exceptions is harmful in language learning"

Connected through hyperedge HE-4 (induced by source paper "cascaded grammatical relation assignment"):
  with members:
    - "representing text chunks"
    - "forgetting exceptions is harmful in language learning"

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)