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: "finer grained entity typing with typenet"
  Abstract: We consider the challenging problem of entity typing over an extremely fine grained set of types, wherein a single mention or entity can have many simultaneous and often hierarchically-structured types. Despite the importance of the problem, there is a relative lack of resources in the form of fine-grained, deep type hierarchies aligned to existing knowledge bases. In response, we introduce TypeNet, a dataset of entity types consisting of over 1941 types organized in a hierarchy, obtained by manually annotating a mapping from 1081 Freebase types to WordNet. We also experiment with several models comparable to state-of-the-art systems and explore techniques to incorporate a structure loss on the hierarchy with the standard mention typing loss, as a first step towards future research on this dataset.

Connected through hyperedge HE-1 (induced by source paper "a neural architecture for person ontology population"):
  with members:
    - "position aware graph neural networks"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "neural fine grained entity type classification with hierarchy aware loss"
    - "ultra fine entity typing"
    - "fine grained classification of personal data entities"
    - "universal sentence encoder"
    - "on2vec embedding based relation prediction for ontology population"
    - "fine grained entity type classification by jointly learning representations and label embeddings"

Connected through hyperedge HE-2 (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-3 (induced by source paper "fine grained entity typing in hyperbolic space"):
  with members:
    - "imposing label relational inductive bias for extremely fine grained entity typing"
    - "hyperbolic attention networks"
    - "skip gram word embeddings in hyperbolic space"
    - "hyperbolic representation learning for fast and efficient neural question answering"
    - "ultra fine entity typing"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "embedding text in hyperbolic spaces"
    - "representation tradeoffs for hyperbolic embeddings"

Connected through hyperedge HE-4 (induced by source paper "new embedded representations and evaluation protocols for inferring transitive relations"):
  with members:
    - "unsupervised hypernym detection by distributional inclusion vector embedding"
    - "learning distributed representations of texts and entities from knowledge base"
    - "an interpretable knowledge transfer model for knowledge base completion"
    - "poincar e embeddings for learning hierarchical representations"
    - "corpus level fine grained entity typing using contextual information"
    - "an attentive neural architecture for fine grained entity type classification"
    - "complex embeddings for simple link prediction"
    - "entity embeddings with conceptual subspaces as a basis for plausible reasoning"

Connected through hyperedge HE-5 (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"
    - "fine grained entity typing with high multiplicity assignments"
    - "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-6 (induced by source paper "fine grained classification of personal data entities"):
  with members:
    - "neural fine grained entity type classification with hierarchy aware loss"
    - "ultra fine entity typing"
    - "fine grained entity type classification by jointly learning representations and label embeddings"
    - "adam a method for stochastic optimization"
    - "context dependent fine grained entity type tagging"

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)