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: "toward a deep neural approach for knowledge based ir"
  Abstract: This paper tackles the problem of the semantic gap between a document and a query within an ad-hoc information retrieval task. In this context, knowledge bases (KBs) have already been acknowledged as valuable means since they allow the representation of explicit relations between entities. However, they do not necessarily represent implicit relations that could be hidden in a corpora. This latter issue is tackled by recent works dealing with deep representation learn ing of texts. With this in mind, we argue that embedding KBs within deep neural architectures supporting documentquery matching would give rise to fine-grained latent representations of both words and their semantic relations. In this paper, we review the main approaches of neural-based document ranking as well as those approaches for latent representation of entities and relations via KBs. We then propose some avenues to incorporate KBs in deep neural approaches for document ranking. More particularly, this paper advocates that KBs can be used either to support enhanced latent representations of queries and documents based on both distributional and relational semantics or to serve as a semantic translator between their latent distributional representations.

Connected through hyperedge HE-1 (induced by source paper "a deep look into neural ranking models for information retrieval"):
  with members:
    - "learning groupwise scoring functions using deep neural networks"
    - "simple applications of bert for ad hoc document retrieval"
    - "semeval 2017 task 3 community question answering"
    - "user intent prediction in information seeking conversations"
    - "vitor learning to rank webpages based on visual features"
    - "wikipassageqa a benchmark collection for research on non factoid answer passage retrieval"
    - "anmm ranking short answer texts with attention based neural matching model"
    - "cross domain regularization for neural ranking models using adversarial learning"

Connected through hyperedge HE-2 (induced by source paper "knowledge representation learning a quantitative review"):
  with members:
    - "improving visual relationship detection using semantic modeling of scene descriptions"
    - "on the equivalence of holographic and complex embeddings for link prediction"
    - "know evolve deep temporal reasoning for dynamic knowledge graphs"
    - "flexible end to end dialogue system for knowledge grounded conversation"
    - "a neural knowledge language model"
    - "analogical inference for multi relational embeddings"
    - "visual translation embedding network for visual relation detection"
    - "probabilistic reasoning via deep learning neural association models"

Connected through hyperedge HE-3 (induced by source paper "dsrim a deep neural information retrieval model enhanced by a knowledge resource driven representation of documents"):
  with members:
    - "neural ranking models with weak supervision"
    - "a deep relevance matching model for ad hoc retrieval"
    - "a dual embedding space model for document ranking"
    - "text matching as image recognition"
    - "convolutional neural network architectures for matching natural language sentences"
    - "distributed representations of sentences and documents"
    - "retrofitting word vectors to semantic lexicons"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-4 (induced by source paper "neural information retrieval a literature review"):
  with members:
    - "lstm a search space odyssey"
    - "tweet2vec character based distributed representations for social media"
    - "using word embeddings for automatic query expansion"
    - "representing documents and queries as sets of word embedded vectors for information retrieval"
    - "using word embeddings in twitter election classification"
    - "a deep relevance matching model for ad hoc retrieval"
    - "a study of matchpyramid models on ad hoc retrieval"
    - "bridging the gap a semantic similarity measure between queries and documents"

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)