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 type classification by jointly learning representations and label embeddings"
  Abstract: Fine-grained entity type classification (FETC) is the task of classifying an entity mention to a broad set of types. Distant supervision paradigm is extensively used to generate training data for this task. However, generated training data assigns same set of labels to every mention of an entity without considering its local context. Existing FETC systems have two major drawbacks: assuming training data to be noise free and use of hand crafted features. Our work overcomes both drawbacks. We propose a neural network model that jointly learns entity mentions and their context representation to eliminate use of hand crafted features. Our model treats training data as noisy and uses non-parametric variant of hinge loss function. Experiments show that the proposed model outperforms previous state-of-the-art methods on two publicly available datasets, namely FIGER (GOLD) and BBN with an average relative improvement of 2.69% in micro-F1 score. Knowledge learnt by our model on one dataset can be transferred to other datasets while using same model or other FETC systems. These approaches of transferring knowledge further improve the performance of respective models.

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 "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-3 (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"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "learning to learn by gradient descent by gradient descent"
    - "neural architectures for fine grained entity type classification"

Connected through hyperedge HE-4 (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"
    - "challenges in data to document generation"
    - "table to text generation by structure aware seq2seq learning"
    - "pointing the unknown words"
    - "context gates for neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "improving distantly supervised entity typing with compact latent space clustering"):
  with members:
    - "a unified labeling approach by pooling diverse datasets for entity typing"
    - "semi supervised learning via compact latent space clustering"
    - "deeptype multilingual entity linking by neural type system evolution"
    - "collective learning from diverse datasets for entity typing in the wild"
    - "attention is all you need"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "corpus level fine grained entity typing using contextual information"
    - "label noise reduction in entity typing by heterogeneous partial label embedding"

Connected through hyperedge HE-6 (induced by source paper "improving fine grained entity typing with entity linking"):
  with members:
    - "improving distantly supervised entity typing with compact latent space clustering"
    - "ultra fine entity typing"
    - "hierarchical losses and new resources for fine grained entity typing and linking"
    - "deep joint entity disambiguation with local neural attention"
    - "building a fine grained entity typing system overnight for a new x x language domain genre"
    - "label noise reduction in entity typing by heterogeneous partial label embedding"
    - "neural architectures for fine grained entity type classification"
    - "context dependent fine grained entity type tagging"

Connected through hyperedge HE-7 (induced by source paper "kcat a knowledge constraint typing annotation tool"):
  with members:
    - "learning to denoise distantly labeled data for entity typing"
    - "improving entity linking by modeling latent relations between mentions"
    - "ultra fine entity typing"
    - "deeptype multilingual entity linking by neural type system evolution"
    - "deep joint entity disambiguation with local neural attention"
    - "label noise reduction in entity typing by heterogeneous partial label embedding"
    - "context dependent fine grained entity type tagging"
    - "introduction to the conll 2000 shared task chunking"

Connected through hyperedge HE-8 (induced by source paper "zero shot open entity typing as type compatible grounding"):
  with members:
    - "ultra fine entity typing"
    - "deep contextualized word representations"
    - "building a fine grained entity typing system overnight for a new x x language domain genre"
    - "neural architectures for fine grained entity type classification"
    - "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)