human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "imposing label relational inductive bias for extremely fine grained entity typing"):
  Abstract: Existing entity typing systems usually exploit the type hierarchy provided by knowledge base (KB) schema to model label correlations and thus improve the overall performance. Such techniques, however, are not directly applicable to more open and practical scenarios where the type set is not restricted by KB schema and includes a vast number of free-form types. To model the underly-ing label correlations without access to manually annotated label structures, we introduce a novel label-relational inductive bias, represented by a graph propagation layer that effectively encodes both global label co-occurrence statistics and word-level similarities.On a large dataset with over 10,000 free-form types, the graph-enhanced model equipped with an attention-based matching module is able to achieve a much higher recall score while maintaining a high-level precision. Specifically, it achieves a 15.3% relative F1 improvement and also less inconsistency in the outputs. We further show that a simple modification of our proposed graph layer can also improve the performance on a conventional and widely-tested dataset that only includes KB-schema types.

Includes members:
  Member "neural fine grained entity type classification with hierarchy aware loss":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a neural architecture for person ontology population")
  Member "ultra fine entity typing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "k adapter infusing knowledge into pre trained models with adapters")
  Member "bridging nonlinearities and stochastic regularizers with gaussian error linear units":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "xgpt cross modal generative pre training for image captioning")
  Member "fine grained entity typing with high multiplicity assignments":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "enteval a holistic evaluation benchmark for entity representations")
  Member "learned in translation contextualized word vectors":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on contextual embeddings")
  Member "label noise reduction in entity typing by heterogeneous partial label embedding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fine grained entity typing in hyperbolic space")
  Member "enhanced lstm for natural language inference":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adversarial filters of dataset biases")
  Member "neural architectures for fine grained entity type classification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a neural architecture for person ontology population")

Question: Please tell me which class the source paper 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)