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: "legal document retrieval using document vector embeddings and deep learning"
  Abstract: Domain specific information retrieval process has been a prominent and ongoing research in the field of natural language processing. Many researchers have incorporated different techniques to overcome the technical and domain specificity and provide a mature model for various domains of interest. The main bottleneck in these studies is the heavy coupling of domain experts, that makes the entire process to be time consuming and cumbersome. In this study, we have developed three novel models which are compared against a golden standard generated via the on line repositories provided, specifically for the legal domain. The three different models incorporated vector space representations of the legal domain, where document vector generation was done in two different mechanisms and as an ensemble of the above two. This study contains the research being carried out in the process of representing legal case documents into different vector spaces, whilst incorporating semantic word measures and natural language processing techniques. The ensemble model built in this study, shows a significantly higher accuracy level, which indeed proves the need for incorporation of domain specific semantic similarity measures into the information retrieval process. This study also shows, the impact of varying distribution of the word similarity measures, against varying document vector dimensions, which can lead to improvements in the process of legal information retrieval.

Connected through hyperedge HE-1 (induced by source paper "shift of perspective identification within legal cases"):
  with members:
    - "identifying relationships among sentences in court case transcripts using discourse relations"
    - "fast approach to build an automatic sentiment annotator for legal domain using transfer learning"
    - "semi supervised instance population of an ontology using word vector embedding"
    - "synergistic union of word2vec and lexicon for domain specific semantic similarity"
    - "deriving a representative vector for ontology classes with instance word vector embeddings"
    - "gov2vec learning distributed representations of institutions and their legal text"

Connected through hyperedge HE-2 (induced by source paper "identifying relationships among sentences in court case transcripts using discourse relations"):
  with members:
    - "semi supervised instance population of an ontology using word vector embedding"
    - "synergistic union of word2vec and lexicon for domain specific semantic similarity"
    - "deriving a representative vector for ontology classes with instance word vector embeddings"

Connected through hyperedge HE-3 (induced by source paper "fast approach to build an automatic sentiment annotator for legal domain using transfer learning"):
  with members:
    - "identifying relationships among sentences in court case transcripts using discourse relations"
    - "gov2vec learning distributed representations of institutions and their legal text"
    - "zero shot learning through cross modal transfer"

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.IR(Information Retrieval)