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: "a walk based model on entity graphs for relation extraction"
  Abstract: We present a novel graph-based neural network model for relation extraction. Our model treats multiple pairs in a sentence simultaneously and considers interactions among them. All the entities in a sentence are placed as nodes in a fully-connected graph structure. The edges are represented with position-aware contexts around the entity pairs. In order to consider different relation paths between two entities, we construct up to l-length walks between each pair. The resulting walks are merged and iteratively used to update the edge representations into longer walks representations. We show that the model achieves performance comparable to the state-of-the-art systems on the ACE 2005 dataset without using any external tools.

Connected through hyperedge HE-1 (induced by source paper "a general framework for information extraction using dynamic span graphs"):
  with members:
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"
    - "neural segmental hypergraphs for overlapping mention recognition"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "deep contextualized word representations"
    - "an annotated corpus for machine reading of instructions in wet lab protocols"
    - "higher order coreference resolution with coarse to fine inference"

Connected through hyperedge HE-2 (induced by source paper "entity relation and event extraction with contextualized span representations"):
  with members:
    - "scibert pretrained contextualized embeddings for scientific text"
    - "a general framework for information extraction using dynamic span graphs"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"
    - "one for all neural joint modeling of entities and events"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "an annotated corpus for machine reading of instructions in wet lab protocols"

Connected through hyperedge HE-3 (induced by source paper "improving neural relation extraction with implicit mutual relations"):
  with members:
    - "predicting semantic relations using global graph properties"
    - "dsgan generative adversarial training for distant supervision relation extraction"
    - "robust distant supervision relation extraction via deep reinforcement learning"
    - "improving distantly supervised relation extraction using word and entity based attention"
    - "reside improving distantly supervised neural relation extraction using side information"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "reinforcement learning for relation classification from noisy data"
    - "neural relation extraction via inner sentence noise reduction and transfer learning"

Connected through hyperedge HE-4 (induced by source paper "docred a large scale document level relation extraction dataset"):
  with members:
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "fewrel a large scale supervised few shot relation classification dataset with state of the art evaluation"
    - "improving the transformer translation model with document level context"
    - "robust distant supervision relation extraction via deep reinforcement learning"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "fever a large scale dataset for fact extraction and verification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "cross sentence n ary relation extraction with graph lstms"

Connected through hyperedge HE-5 (induced by source paper "connecting the dots document level neural relation extraction with edge oriented graphs"):
  with members:
    - "relation extraction using explicit context conditioning"
    - "document level n ary relation extraction with multiscale representation learning"
    - "a general framework for information extraction using dynamic span graphs"
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "neural relation extraction within and across sentence boundaries"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "reside improving distantly supervised neural relation extraction using side information"
    - "convolutional neural networks for chemical disease relation extraction are improved with character based word embeddings"

Connected through hyperedge HE-6 (induced by source paper "graph neural networks with generated parameters for relation extraction"):
  with members:
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "improving entity linking by modeling latent relations between mentions"
    - "question answering by reasoning across documents with graph convolutional networks"
    - "cross sentence n ary relation extraction with graph lstms"
    - "a simple neural network module for relational reasoning"
    - "scene graph generation by iterative message passing"
    - "neural message passing for quantum chemistry"
    - "encoding sentences with graph convolutional networks for semantic role labeling"

Connected through hyperedge HE-7 (induced by source paper "comprehend medical a named entity recognition and relationship extraction web service"):
  with members:
    - "relation extraction using explicit context conditioning"
    - "joint entity extraction and assertion detection for clinical text"
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "dynamic transfer learning for named entity recognition"
    - "a hybrid deep learning approach for medical relation extraction"
    - "improving hospital mortality prediction with medical named entities and multimodal learning"
    - "global normalization of convolutional neural networks for joint entity and relation classification"
    - "joint extraction of entities and relations based on a novel tagging scheme"

Connected through hyperedge HE-8 (induced by source paper "relation extraction using explicit context conditioning"):
  with members:
    - "joint entity extraction and assertion detection for clinical text"
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "self attention with relative position representations"
    - "a hybrid deep learning approach for medical relation extraction"
    - "improving hospital mortality prediction with medical named entities and multimodal learning"
    - "attention is all you need"
    - "joint extraction of entities and relations based on a novel tagging scheme"
    - "a survey of deep learning methods for relation extraction"

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)