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: "bag bi directional attention entity graph convolutional network for multi hop reasoning question answering"
  Abstract: Multi-hop reasoning question answering requires deep comprehension of relationships between various documents and queries. We propose a Bi-directional Attention Entity Graph Convolutional Network (BAG), leveraging relationships between nodes in an entity graph and attention information between a query and the entity graph, to solve this task. Graph convolutional networks are used to obtain a relation-aware representation of nodes for entity graphs built from documents with multi-level features. Bidirectional attention is then applied on graphs and queries to generate a query-aware nodes representation, which will be used for the final prediction. Experimental evaluation shows BAG achieves state-of-the-art accuracy performance on the QAngaroo WIKIHOP dataset.

Connected through hyperedge HE-1 (induced by source paper "differentiable reasoning over a virtual knowledge base"):
  with members:
    - "latent retrieval for weakly supervised open domain question answering"
    - "explore propose and assemble an interpretable model for multi hop reading comprehension"
    - "pullnet open domain question answering with iterative retrieval on knowledge bases and text"
    - "cognitive graph for multi hop reading comprehension at scale"
    - "neural query language a knowledge base query language for tensorflow"
    - "multi hop paragraph retrieval for open domain question answering"
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "what do you learn from context probing for sentence structure in contextualized word representations"

Connected through hyperedge HE-2 (induced by source paper "heterogeneous graph learning for visual commonsense reasoning"):
  with members:
    - "image question answer synergistic network for visual dialog"
    - "tactical rewind self correction via backtracking in vision and language navigation"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "information maximizing visual question generation"
    - "from recognition to cognition visual commonsense reasoning"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "learning to compose dynamic tree structures for visual contexts"
    - "deep contextualized word representations"

Connected through hyperedge HE-3 (induced by source paper "select answer and explain interpretable multi hop reading comprehension over multiple documents"):
  with members:
    - "coarse grain fine grain coattention network for multi evidence question answering"
    - "answering while summarizing multi task learning for multi hop qa with evidence extraction"
    - "understanding dataset design choices for multi hop reasoning"
    - "evidence sentence extraction for machine reading comprehension"
    - "multi hop reading comprehension across multiple documents by reasoning over heterogeneous graphs"
    - "roberta a robustly optimized bert pretraining approach"
    - "densely connected attention propagation for reading comprehension"
    - "an interpretable reasoning network for multi relation question answering"

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)