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: "neural fine grained entity type classification with hierarchy aware loss"
  Abstract: The task of Fine-grained Entity Type Classification (FETC) consists of assigning types from a hierarchy to entity mentions in text. Existing methods rely on distant supervision and are thus susceptible to noisy labels that can be out-of-context or overly-specific for the training sentence. Previous methods that attempt to address these issues do so with heuristics or with the help of hand-crafted features. Instead, we propose an end-to-end solution with a neural network model that uses a variant of cross- entropy loss function to handle out-of-context labels, and hierarchical loss normalization to cope with overly-specific ones. Also, previous work solve FETC a multi-label classification followed by ad-hoc post-processing. In contrast, our solution is more elegant: we use public word embeddings to train a single-label that jointly learns representations for entity mentions and their context. We show experimentally that our approach is robust against noise and consistently outperforms the state-of-the-art on established benchmarks for the task.

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"
    - "ultra fine entity typing"
    - "fine grained classification of personal data entities"
    - "universal sentence encoder"
    - "on2vec embedding based relation prediction for ontology population"
    - "finer grained entity typing with typenet"
    - "fine grained entity type classification by jointly learning representations and label embeddings"

Connected through hyperedge HE-2 (induced by source paper "imposing label relational inductive bias for extremely fine grained entity typing"):
  with members:
    - "ultra fine entity typing"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "fine grained entity typing with high multiplicity assignments"
    - "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 "collective learning from diverse datasets for entity typing in the wild"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "fine grained entity type classification by jointly learning representations and label embeddings"
    - "yolo9000 better faster stronger"
    - "neural architectures for fine grained entity type classification"
    - "noise mitigation for neural entity typing and relation extraction"
    - "a universal part of speech tagset"
    - "introduction to the conll 2003 shared task language independent named entity recognition"

Connected through hyperedge HE-4 (induced by source paper "fine grained classification of personal data entities"):
  with members:
    - "ultra fine entity typing"
    - "finer grained entity typing with typenet"
    - "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)