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: "forecasting social navigation in crowded complex scenes"
  Abstract: When humans navigate a crowed space such as a university campus or the sidewalks of a busy street, they follow common sense rules based on social etiquette. In this paper, we argue that in order to enable the design of new algorithms that can take fully advantage of these rules to better solve tasks such as target tracking or trajectory forecasting, we need to have access to better data in the first place. To that end, we contribute the very first large scale dataset (to the best of our knowledge) that collects images and videos of various types of targets (not just pedestrians, but also bikers, skateboarders, cars, buses, golf carts) that navigate in a real-world outdoor environment such as a university campus. We present an extensive evaluation where different methods for trajectory forecasting are evaluated and compared. Moreover, we present a new algorithm for trajectory prediction that exploits the complexity of our new dataset and allows to: i) incorporate inter-class interactions into trajectory prediction models (e.g, pedestrian vs bike) as opposed to just intra-class interactions (e.g., pedestrian vs pedestrian); ii) model the degree to which the social forces are regulating an interaction. We call the latter "social sensitivity"and it captures the sensitivity to which a target is responding to a certain interaction. An extensive experimental evaluation demonstrates the effectiveness of our novel approach.

Connected through hyperedge HE-1 (induced by source paper "multipath multiple probabilistic anchor trajectory hypotheses for behavior prediction"):
  with members:
    - "rules of the road predicting driving behavior with a convolutional model of semantic interactions"
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "generative modeling of multimodal multi human behavior"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "accurate and diverse sampling of sequences based on a best of many sample objective"
    - "desire distant future prediction in dynamic scenes with interacting agents"

Connected through hyperedge HE-2 (induced by source paper "car net clairvoyant attentive recurrent network"):
  with members:
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "vunet dynamic scene view synthesis for traversability estimation using an rgb camera"
    - "gonet a semi supervised deep learning approach for traversability estimation"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "to go or not to go a near unsupervised learning approach for robot navigation"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "diversified visual attention networks for fine grained object classification"
    - "image captioning with semantic attention"

Connected through hyperedge HE-3 (induced by source paper "motion prediction under multimodality with conditional stochastic networks"):
  with members:
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "visual dynamics probabilistic future frame synthesis via cross convolutional networks"
    - "interaction networks for learning about objects relations and physics"
    - "an uncertain future forecasting from static images using variational autoencoders"
    - "video pixel networks"
    - "generating videos with scene dynamics"
    - "end to end learning of action detection from frame glimpses in videos"
    - "spatio temporal video autoencoder with differentiable memory"

Connected through hyperedge HE-4 (induced by source paper "knowledge transfer for scene specific motion prediction"):
  with members:
    - "discovery of shared semantic spaces for multiscene video query and summarization"
    - "a data driven approach for tag refinement and localization in web videos"
    - "learning to divide and conquer for online multi target tracking"
    - "anticipating visual representations from unlabeled video"
    - "joint inference of groups events and human roles in aerial videos"
    - "anticipating the future by watching unlabeled video"

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)