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: "visual reasoning with multi hop feature modulation"
  Abstract: Recent breakthroughs in computer vision and natural language processing have spurred interest in challenging multi-modal tasks such as visual question-answering and visual dialogue. For such tasks, one successful approach is to condition image-based convolutional network computation on language via Feature-wise Linear Modulation (FiLM) layers, i.e., per-channel scaling and shifting. We propose to generate the parameters of FiLM layers going up the hierarchy of a convolutional network in a multi-hop fashion rather than all at once, as in prior work. By alternating between attending to the language input and generating FiLM layer parameters, this approach is better able to scale to settings with longer input sequences such as dialogue. We demonstrate that multi-hop FiLM generation achieves state-of-the-art for the short input sequence task ReferIt --- on-par with single-hop FiLM generation --- while also significantly outperforming prior state-of-the-art and single-hop FiLM generation on the GuessWhat?! visual dialogue task.

Connected through hyperedge HE-1 (induced by source paper "fine grained image to image transformation towards visual recognition"):
  with members:
    - "a two step disentanglement method"
    - "hologan unsupervised learning of 3d representations from natural images"
    - "foreground aware image inpainting"
    - "carigan caricature generation through weakly paired adversarial learning"
    - "visual object networks image generation with disentangled 3d representation"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "cr gan learning complete representations for multi view generation"
    - "multi task convolutional neural network for pose invariant face recognition"

Connected through hyperedge HE-2 (induced by source paper "mutatt visual textual mutual guidance for referring expression comprehension"):
  with members:
    - "learning to collocate neural modules for image captioning"
    - "dynamic graph attention for referring expression comprehension"
    - "visual semantic reasoning for image text matching"
    - "neighbourhood watch referring expression comprehension via language guided graph attention networks"
    - "mattnet modular attention network for referring expression comprehension"
    - "rethinking diversified and discriminative proposal generation for visual grounding"
    - "identity aware textual visual matching with latent co attention"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"

Connected through hyperedge HE-3 (induced by source paper "referring expression grounding by marginalizing scene graph likelihood"):
  with members:
    - "learning to compose and reason with language tree structures for visual grounding"
    - "using syntax to ground referring expressions in natural images"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "neighbourhood watch referring expression comprehension via language guided graph attention networks"
    - "mattnet modular attention network for referring expression comprehension"
    - "rethinking diversified and discriminative proposal generation for visual grounding"
    - "relational inductive biases deep learning and graph networks"
    - "visual question reasoning on general dependency tree"

Connected through hyperedge HE-4 (induced by source paper "videonavqa bridging the gap between visual and embodied question answering"):
  with members:
    - "embodied question answering in photorealistic environments with point cloud perception"
    - "multi target embodied question answering"
    - "benchmarking classic and learned navigation in complex 3d environments"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "habitat a platform for embodied ai research"
    - "murel multimodal relational reasoning for visual question answering"
    - "learning visual question answering by bootstrapping hard attention"
    - "systematic generalization what is required and can it be learned"

Connected through hyperedge HE-5 (induced by source paper "improving referring expression grounding with cross modal attention guided erasing"):
  with members:
    - "bilinear attention networks"
    - "dropblock a regularization method for convolutional networks"
    - "stacked cross attention for image text matching"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "adversarial complementary learning for weakly supervised object localization"
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "mattnet modular attention network for referring expression comprehension"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"

Connected through hyperedge HE-6 (induced by source paper "attention based conditioning methods for external knowledge integration"):
  with members:
    - "utilizing neural networks and linguistic metadata for early detection of depression indications in text sequences"
    - "gating mechanisms for combining character and word level word representations an empirical study"
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "deep contextualized word representations for detecting sarcasm and irony"
    - "robust lexical features for improved neural network named entity recognition"
    - "ntua slp at iest 2018 ensemble of neural transfer methods for implicit emotion classification"
    - "deep contextualized word representations"
    - "reside improving distantly supervised neural relation extraction using side information"

Connected through hyperedge HE-7 (induced by source paper "meta graph few shot link prediction via meta learning"):
  with members:
    - "learning to propagate for graph meta learning"
    - "meta relational learning for few shot link prediction in knowledge graphs"
    - "gnn film graph neural networks with feature wise linear modulation"
    - "link prediction based on graph neural networks"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "meta learning with latent embedding optimization"
    - "how to train your maml"
    - "one shot relational learning for knowledge graphs"

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.CV(Computer Vision and Pattern Recognition)