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: "fine grained entity typing with high multiplicity assignments"
  Abstract: As entity type systems become richer and more fine-grained, we expect the number of types assigned to a given entity to increase. However, most fine-grained typing work has focused on datasets that exhibit a low degree of type multiplicity. In this paper, we consider the high-multiplicity regime inherent in data sources such as Wikipedia that have semi-open type systems. We introduce a set-prediction approach to this problem and show that our model outperforms unstructured baselines on a new Wikipedia-based fine-grained typing corpus.

Connected through hyperedge HE-1 (induced by source paper "enteval a holistic evaluation benchmark for entity representations"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "joint learning of named entity recognition and entity linking"
    - "barack s wife hillary using knowledge graphs for fact aware language modeling"
    - "evaluation benchmarks and learning criteriafor discourse aware sentence representations"
    - "ernie enhanced representation through knowledge integration"
    - "zero shot entity linking by reading entity descriptions"
    - "socialiqa commonsense reasoning about social interactions"
    - "seeing things from a different angle discovering diverse perspectives about claims"

Connected through hyperedge HE-2 (induced by source paper "imposing label relational inductive bias for extremely fine grained entity typing"):
  with members:
    - "neural fine grained entity type classification with hierarchy aware loss"
    - "ultra fine entity typing"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "learned in translation contextualized word vectors"
    - "label noise reduction in entity typing by heterogeneous partial label embedding"
    - "enhanced lstm for natural language inference"
    - "neural architectures for fine grained entity type classification"
    - "an attentive neural architecture for fine grained entity type classification"

Connected through hyperedge HE-3 (induced by source paper "ultra fine entity typing"):
  with members:
    - "deeptype multilingual entity linking by neural type system evolution"
    - "end to end neural coreference resolution"
    - "finer grained entity typing with typenet"
    - "fine grained entity type classification by jointly learning representations and label embeddings"
    - "label noise reduction in entity typing by heterogeneous partial label embedding"
    - "cotype joint extraction of typed entities and relations with knowledge bases"

Connected through hyperedge HE-4 (induced by source paper "multi multi view learning multilingual and multi representation entity typing"):
  with members:
    - "an attention based collaboration framework for multi view network representation learning"
    - "enriching word vectors with subword information"
    - "noise mitigation for neural entity typing and relation extraction"
    - "inferring missing entity type instances for knowledge base completion new dataset and methods"
    - "a survey on multi view learning"

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)