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 architectures for fine grained entity type classification"
  Abstract: In this work, we investigate several neural network architectures for fine-grained entity type classification. Particularly, we consider extensions to a recently proposed attentive neural architecture and make three key contributions. Previous work on attentive neural architectures do not consider hand-crafted features, we combine learnt and hand-crafted features and observe that they complement each other. Additionally, through quantitative analysis we establish that the attention mechanism is capable of learning to attend over syntactic heads and the phrase containing the mention, where both are known strong hand-crafted features for our task. We enable parameter sharing through a hierarchical label encoding method, that in low-dimensional projections show clear clusters for each type hierarchy. Lastly, despite using the same evaluation dataset, the literature frequently compare models trained using different data. We establish that the choice of training data has a drastic impact on performance, with decreases by as much as 9.85% loose micro F1 score for a previously proposed method. Despite this, our best model achieves state-of-the-art results with 75.36% loose micro F1 score on the well- established FIGER (GOLD) 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"
    - "finer grained entity typing with typenet"

Connected through hyperedge HE-2 (induced by source paper "probing for semantic classes diagnosing the meaning content of word embeddings"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "semantic structure and interpretability of word embeddings"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "conditional generators of words definitions"
    - "factors influencing the surprising instability of word embeddings"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"

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 "learning to learn and predict a meta learning approach for multi label classification"):
  with members:
    - "reinforced co training"
    - "deep reinforcement learning that matters"
    - "doctag2vec an embedding based multi label learning approach for document tagging"
    - "prototypical networks for few shot learning"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "fine grained entity type classification by jointly learning representations and label embeddings"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "learning to learn by gradient descent by gradient descent"

Connected through hyperedge HE-5 (induced by source paper "ensuring readability and data fidelity using head modifier templates in deep type description generation"):
  with members:
    - "generating fine grained open vocabulary entity type descriptions"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "get to the point summarization with pointer generator networks"
    - "fine grained entity type classification by jointly learning representations and label embeddings"
    - "challenges in data to document generation"
    - "table to text generation by structure aware seq2seq learning"
    - "pointing the unknown words"

Connected through hyperedge HE-6 (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"
    - "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"
    - "an attentive neural architecture for fine grained entity type classification"

Connected through hyperedge HE-7 (induced by source paper "case context aware semantic expansion"):
  with members:
    - "setexpan corpus based set expansion via context feature selection and rank ensemble"
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "deep contextualized word representations"
    - "effective approaches to attention based neural machine translation"
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "convolutional neural network architectures for matching natural language sentences"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-8 (induced by source paper "fine grained entity recognition with reduced false negatives and large type coverage"):
  with members:
    - "hierarchical losses and new resources for fine grained entity typing and linking"
    - "corpus level fine grained entity typing"
    - "label noise reduction in entity typing by heterogeneous partial label embedding"
    - "neural architectures for named entity recognition"
    - "named entity recognition with bidirectional lstm cnns"
    - "polyglot ner massive multilingual named entity recognition"
    - "context dependent fine grained entity type tagging"
    - "introduction to the conll 2003 shared task language independent named entity recognition"

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)