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: "video relationship reasoning using gated spatio temporal energy graph"
  Abstract: Visual relationship reasoning is a crucial yet challenging task for understanding rich interactions across visual concepts. For example, a relationship 'man, open, door' involves a complex relation 'open' between concrete entities 'man, door'. While much of the existing work has studied this problem in the context of still images, understanding visual relationships in videos has received limited attention. Due to their temporal nature, videos enable us to model and reason about a more comprehensive set of visual relationships, such as those requiring multiple (temporal) observations (e.g., 'man, lift up, box' vs. 'man, put down, box'), as well as relationships that are often correlated through time (e.g., 'woman, pay, money' followed by 'woman, buy, coffee'). In this paper, we construct a Conditional Random Field on a fully-connected spatio-temporal graph that exploits the statistical dependency between relational entities spatially and temporally. We introduce a novel gated energy function parametrization that learns adaptive relations conditioned on visual observations. Our model optimization is computationally efficient, and its space computation complexity is significantly amortized through our proposed parameterization. Experimental results on benchmark video datasets (ImageNet Video and Charades) demonstrate state-of-the-art performance across three standard relationship reasoning tasks: Detection, Tagging, and Recognition.

Connected through hyperedge HE-1 (induced by source paper "zero shot generation of human object interaction videos"):
  with members:
    - "cgans with projection discriminator"
    - "recycle gan unsupervised video retargeting"
    - "spectral normalization for generative adversarial networks"
    - "probabilistic video generation using holistic attribute control"
    - "large scale gan training for high fidelity natural image synthesis"
    - "videos as space time region graphs"
    - "image generation from scene graphs"
    - "learning to decompose and disentangle representations for video prediction"

Connected through hyperedge HE-2 (induced by source paper "recurrent space time graph neural networks"):
  with members:
    - "recurrent space time graphs for video understanding"
    - "universal transformers"
    - "eco efficient convolutional network for online video understanding"
    - "motion feature network fixed motion filter for action recognition"
    - "relational recurrent neural networks"
    - "relational inductive biases deep learning and graph networks"
    - "how powerful are graph neural networks"
    - "videos as space time region graphs"

Connected through hyperedge HE-3 (induced by source paper "transformer dissection an unified understanding for transformer s attention via the lens of kernel"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "transformer xl attentive language models beyond a fixed length context"
    - "multimodal transformer for unaligned multimodal language sequences"
    - "generating long sequences with sparse transformers"
    - "on the turing completeness of modern neural network architectures"
    - "set transformer a framework for attention based permutation invariant neural networks"
    - "image transformer"
    - "self attention with relative position representations"

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)