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: "shotgunwsd an unsupervised algorithm for global word sense disambiguation inspired by dna sequencing"
  Abstract: In this paper, we present a novel unsupervised algorithm for word sense disambiguation (WSD) at the document level. Our algorithm is inspired by a widely-used approach in the field of genetics for whole genome sequencing, known as the Shotgun sequencing technique. The proposed WSD algorithm is based on three main steps. First, a brute-force WSD algorithm is applied to short context windows (up to 10 words) selected from the document in order to generate a short list of likely sense configurations for each window. In the second step, these local sense configurations are assembled into longer composite configurations based on suffix and prefix matching. The resulted configurations are ranked by their length, and the sense of each word is chosen based on a voting scheme that considers only the top k configurations in which the word appears. We compare our algorithm with other state-of-the-art unsupervised WSD algorithms and demonstrate better performance, sometimes by a very large margin. We also show that our algorithm can yield better performance than the Most Common Sense (MCS) baseline on one data set. Moreover, our algorithm has a very small number of parameters, is robust to parameter tuning, and, unlike other bio-inspired methods, it gives a deterministic solution (it does not involve random choices).

Connected through hyperedge HE-1 (induced by source paper "vector of locally aggregated word embeddings vlawe a novel document level representation"):
  with members:
    - "automated essay scoring with string kernels and word embeddings"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "from image to text classification a novel approach based on clustering word embeddings"
    - "dynamic compositional neural networks over tree structure"
    - "text classification improved by integrating bidirectional lstm with two dimensional max pooling"
    - "learning distributed representations of sentences from unlabelled data"
    - "skip thought vectors"

Connected through hyperedge HE-2 (induced by source paper "a novel neural sequence model with multiple attentions for word sense disambiguation"):
  with members:
    - "one single deep bidirectional lstm network for word sense disambiguation of text data"
    - "a game theoretic approach to word sense disambiguation"
    - "semi supervised word sense disambiguation with neural models"
    - "word sense disambiguation using a bidirectional lstm"
    - "linguistic input features improve neural machine translation"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-3 (induced by source paper "a short survey on sense annotated corpora"):
  with members:
    - "language modelling makes sense propagating representations through wordnet for full coverage word sense disambiguation"
    - "glossbert bert for word sense disambiguation with gloss knowledge"
    - "incorporating glosses into neural word sense disambiguation"
    - "huge automatically extracted training sets for multilingual word sense disambiguation"
    - "named entity disambiguation for noisy text"
    - "embedding words and senses together via joint knowledge enhanced training"
    - "semi supervised word sense disambiguation with neural models"
    - "word sense disambiguation using a bidirectional lstm"

Connected through hyperedge HE-4 (induced by source paper "a short survey on sense annotated corpora for diverse languages and resources"):
  with members:
    - "huge automatically extracted training sets for multilingual word sense disambiguation"
    - "knowledge based word sense disambiguation using topic models"
    - "named entity disambiguation for noisy text"
    - "semi supervised word sense disambiguation with neural models"
    - "word sense disambiguation using a bidirectional lstm"

Connected through hyperedge HE-5 (induced by source paper "unibuckernel a kernel based learning method for complex word identification"):
  with members:
    - "complex word identification challenges in data annotation and system performance"

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)